Thinking Improv

Promotion Dance

2012-02-07T18:41:00.000-08:00

Want to know the secret to being promoted? Keen to ascend the ladder of success? Fancy seeing your hard work appreciated and reflected in a more senior position? Improv has the answer. Well, to be more exact, improv plus a healthy combination of agent-based modeling and behavior science has the answer. Or, at least, if not actually a complete answer, then at the very least a healthy dose of profound, if not slightly chilling insight.

Let me explain. One of the more compelling games that came out of Behavior Lab last year was something I call Promotion Dance. This was a game that I dreamt up during brainstorming sessions with the excellent Dave Sals. It grew out of a desire to create games that explained things about workplace dynamics, and also from some agent-based modeling work on age discrimination that I've been doing with Rich Martell, a fascinating and highly astute organizational psychologist I met recently.

To play this game you need a large group. Twenty people or so works well. These people each have one of three roles. CEO (you only have one of these), Team Lead (you need about three or four of these), and Team Members (everyone else). You arrange the people as follows:

Then, once everyone is positioned, you start dancing. The CEO's job is to communicate his dance choices to his team leads. The team leads' job is to communicate the CEO's choices to the teams. And the teams' job is to accurately reflect the dance they're seeing their team lead do. Team members are encouraged at the outset to focus on their team lead, not the CEO.

All that's left is to add the extra twist: that occasionally the facilitator will shout: Promotion! When that happens, the CEO has to choose the team lead he thinks is doing the best job. Then that team lead has to promote the member of their team who they in turn think is doing the best job. The selected team lead becomes the new CEO. The team member becomes the new team lead, and the CEO (this is where it gets unrealistic) fills the slot left by the promoted team member. The game then proceeds as before.

Promotion Dance is fun to play. People generally enjoy the dancing, and the sense of power that comes from being either a team lead or a CEO. However, it's what's revealed when a promotion happens that makes the game fascinating.

The first thing you notice is that without being asked, the CEO generally assumes that the best team lead is the one whose dance is most similar to their own, even though this has never been stated. The next fascinating thing is that the team leads have all been watching the CEO rather than their teams. This means that when one of them is picked, they suddenly remember that their teams exist and choose someone almost at random. What's even more fascinating is that these patterns persist, pretty much unaffected, even after people have started to figure out what's going on.

What do we learn from this?

Plenty. As always, we have to take the findings with a pinch of salt, because this is a game, not a real workplace. However, it's pretty clear that:

When not given guidance to the contrary, people instinctively measure the quality of a person's work based on the attention they receive from that person, rather than from other measures that would require more work to evaluate. (This is because direct human attention is both rewarding and easy to assess. It fulfills our natural desire to have an effect on the world that is immediate, clear, and effortless to discern.)
That when there are more than two levels in an organization, that the tension on those in the middle layers will always be resolved by those people directing more of their attention up the hierarchy than down. (This is because those who direct their attention down are less likely to get ahead, and will usually be replaced by those who direct more of their attention up.)
That people who are below two levels of management must pay very close attention to their boss in order to be noticed, as the time they have to achieve this will be a small fraction of the time that their boss can possibly afford to give while remaining in the game.

This demonstration is simplistic and undoubtedly we could muddy the waters with many different additions to the dynamic. However the point that the game makes when played is extremely clear. Without awareness and care, people automatically construct hierarchies that reward sycophancy. They don't do this because they're bad people, or have selfish personalities, or because their culture is toxic. It's simply the emergent effect of a hierarchical arrangement of people, all of whom want to receive attention.

It would be easy to say at this point that, sure, we know this happens. It's been going on for thousands of years. But that modern organizations use tools like assessment centers and 360 interviews to choose the people to promote. And while that's a great start it misses the core point. And that's that the recommendations for who to assess still come from somewhere, because HR departments can't watch everyone all the time. As we all know, tools for measuring the effectiveness of employees in their current roles won't really help you either, because you're assessing for talent in the position you're about to take your employee out of.

The easy, dangerous lesson we could absorb here is that kissing up to the boss really does help, for solid, testable reasons. The better lesson, I think, is that organizations that want to keep their culture healthy have to take active steps to avoid the promotion-dance-effect.

This starts with the CEO. Rather than looking just at his team leads because that's easy, he needs to be looking at the employees below to see what they're actually doing. However, it doesn't end there. If bottom-line employees want to be taken seriously, they need to make sure they're visible to people higher up the organization than their direct manager, and sharing clear information about their experiences whenever possible.

The right lesson, if you like, is that strong, healthy organizations dance together, rather than in a hierarchy.

Archetypal Improv Revisited 2

2012-02-03T19:04:00.000-08:00

In the name of clarity, fun, and general, all-purpose niceness, I hereby present some of the notes I recently shared with Opening Night Theater on the Vanilla Six Hander Format. The original question was: how do I teach it? My reply (now somewhat tidied up) was as follows:

First, I make sure people have a strong grounding in status and listening skills.
Then, I set expectations. Getting really good at full-length plays that might have been scripted usually takes about a year to really master. Also, some people find collaborating on complete plots a bit of a head-scrambler. People who just want to have fun on stage and don't enjoy having their brains burst often bow out before they absorb all the skills.
I introduce the basic structure and why it's used.
I run a workshop on getting into trouble. This often involves teaching tilts, escalation scenes, and some coached play openings.
I teach just Scene 1 and give people plenty of opportunities to explore it until it's starting to come naturally. This sometimes comes with a small shock, because people are used to 'starting positive' to build a platform and avoiding 'instant trouble'. I encourage people to start working on dramatic tension from the first line if the story needs it by realizing that the best way to help a fellow improviser can often be to put them in an awkward situation that paints a sympathetic picture to the audience.
I teach just Scene 2 without a scene 1 preceding it, so that people can get a sense of what it feels like in isolation.
I build to Scene 1 followed by Scene 2, so that people can explore linking the two together and picking out themes that will work together.
Once players have stabilized on how to link scenes together thematically while keeping the content distinct, I teach 1,2,3.
I start getting people to ask the following questions. What is going to be the lowest moment for the protagonist? What is the best way for this story to end? I emphasize the fact that everyone's version of the obvious answers are going to be different, and that's okay. I also try to encourage people to actually be able to go dark in terms of plot content. Players, even very experienced ones, often find this very hard to do well. A lot of improv is taught with the mantra 'be positive and always say yes'. When you're doing long form, you often have to hurt a character to help a fellow improviser.
I start coaching plays, letting them extrapolate forwards, usually encouraging players to drop a play at the point at which the troupe is feeling lost and start again. When a lot of scenes have been invested in a play, it's somewhat harder to think of it as 'disposable theater'.
I coach players towards delivering a complete play, trying to help them see ways to get the protagonist in and out of trouble with escalation each time without making choices for them.
When the troupe is able to pull off a complete play that they feel happy with, celebration is compulsory.
Throughout the process, I watch the interpersonal dynamics very carefully. Constructing a full play is a challenge for many improvisers and requires developing new skills. That's often intensely rewarding for them but also tricky because some people pick it up faster than others. Also, some people inhabit their characters as they go, while others suddenly focus on plot and forget how to act. All this can cause friction that needs to be managed.
I also make sure that I have at least one pencil and paper session in which I teach the troupe how to rapidly plot a movie or novel using the sequential approach. This provides a second lens through which to view the story-building process.

Archetypal Improv Revisited

2012-01-30T18:42:00.000-08:00

A few weeks ago I learned that the lovely people at Opening Night Theater in Toronto wanted to explore the Vanilla Six Hander improv play format that I outlined on this blog back in April 2010. I was delighted.

I’ve been chatting with them via email since then, and thought it was about time to try to collect those thoughts, along with a few others. What I’m hoping to achieve in the next few posts is to give people a clearer sense of how best to explore the format, what its quirks are, and what kind of results it can deliver. First, some history.

The V6H grew out of two converging trains of thought that emerged out of the work I did with Amazing Spectacles in Cambridge around the turn of the millennium. The first of these was watching what happened when people tried to do unstructured improv plays. We were doing a lot of full-length plays at that point, and had various heuristics for how to build them. However, some clearly worked a lot better than others. Furthermore, the plays that worked had certain key features in common:

A strong rapidly-defined platform
Clear characters, but also clear character roles
Threads that started somewhat separate but which joined into a single satisfying story arc
Improvisers who were synced, in terms of their mood, skill levels, and expectations

From working with Patti Stiles in London, and from Freestye Rep in New York, I was familiar with both the idea of rising narrative tension, and Kenn Adam’s ‘story spine’ model. However, I had the sense that really good improv plays had certain symmetries that weren’t captured explicitly in either of these approaches.

Around the same time, Gary Mooney (one of the most naturally gifted comic improvisers I have ever met) pointed me at a book he had been reading called The Writer’s Journey, by Christopher Vogler. It was clear to me very quickly that the content in Vogler’s book had enormous implications for improv.

The Writer’s Journey is a tidy encapsulation of Joseph Campbell’s work on the monomyth from the writer’s perspective. Though it was intended for an audience of screenwriters rather than improvisers, it revealed patterns that occur in many of the great tales that have been told since the start of civilization--tales that were usually spoken rather than seen. In effect, it laid bare the mechanics of storytelling in any form.

For that reason, when it comes screenwriting, I take Vogler’s recipe with a pinch of salt, which I think he’d consider appropriate. This is because strict adherence to the monomyth pattern can produce stories that bear an uncanny resemblance to The Matrix or Star Wars, and which suffer a little in the subtlety department. You have to deviate from any recipe to make a story come alive, no matter how good that recipe is.

However, for improvisers, a strong, specific awareness of what makes a great story represents a massive advantage. This is because improvised stories always deviate from the recipe. The trick is to provide all your actors with a shared understanding of how to support each other and what direction to head in. Knowing how great stories are built, and how they’ve persisted for thousands of years, gives improvisers a golden compass to follow as they navigate the massive uncertainties of their art.

The problem we had was that the recipe that Vogler described was a pattern of sequential steps. In my experience, trying to hold an improv play to any specific sequence is tricky, and often problematic. The work that Kenn Adams has done seems to me to take this approach about as far as you reasonably can. Beyond that, you have too much structure in the work and the quality of the improv starts to deteriorate.

What my troupe in Cambridge needed was a parallelized version of the same approach. A model that created a platform so strong that the seeds of a great story arc were already latent within it. The V6H is still my best attempt to date to achieve that. It focuses on encouraging improvisers to take on archetypal roles that let the play feel rounded and purposeful while being truly improvised at the same time. The V6H works by structuring the first three scenes of the play fairly tightly and keeping the rest loose. However, once the principles of archetypal improv are embedded in actors’ minds, it’s safe to set down the structured introduction. By then, you usually know what a certain character is useful for about ten seconds after he or she walks on stage.

I have seen the V6H produce some really wonderful improvised theater. I’ve had the good fortune to watch as well as coach, and have seen it deliver some of the most funny, touching, shocking, dramatic, impressive long-format improv that I’ve ever witnessed. Furthermore, V6H plays have a very different feeling from those where the players simply rely on good listening and shared experience to guide them, even when the improvisers are very talented. Or from those plays that employ a platform-development-resolution kind of structure and don’t dig any deeper. That’s because V6H plays have a real shape. They breathe in and out like living things.

Having said that though, here some important points that anyone trying the V6H should bear in mind.

It’s not really mine

Though I have taken credit here for the V6H, the truth is that it was, and still is, a massive collective effort. Without Gary’s initial input, and without the significant refinements contributed by Dennis Howlett, Netta Shamir, Justin Lamb, and others, it’s unlikely that it would have ever got off the ground. The V6H then continued to evolve when I moved to Santa Cruz. More marvelous people, like Cindy Ventrice, Dave Sals, Tish Eastman, and Karen Menahan, helped me shape it. And the work is still going on in great troupes like Six Wheel Drive.

Because of this, there is no one set of rules for the V6H. There is no handbook (yet) and no notion of exact right and wrong. The format belongs to the people who are doing it. I think this is wonderful because it means it’s always evolving. However, its openness comes with difficulties because figuring out the perfect solution of how to use the V6H with your troupe is always something that will have to be figured out afresh.

The pain comes before the gains

Another key point is that building an understanding of plot and archetype into improv plays comes with costs before it comes with advantages. It can take people over a year to internalize the core principles. During that time, improvisers, often brilliant ones, will deliver a great number of wooden, clunky characters and terrible plot offers while they figure out how to integrate their new knowledge. This is because it's hard to serve the story and your character at the same time until you understand how the two sides fit together. It’s easy to feel like you’re going backwards during this time. Its only afterwards, when storytelling becomes this marvelous, collective, instinctive force, that you get all your original skills back.

There will be resistance

I have encountered a lot of improvisers (some of them really good) who don’t want to believe that great stories have a shape. I have encountered writers, playwrights, and screenwriters who believe this too. Even though many of these people tap into exactly the same patterns to make their work fly, they do so in a subconscious fashion without ever letting onto themselves what’s happening. The net is, if you’re running a troupe and people don’t want to believe that stories have structure, that’s a problem. It’s usually best under those circumstances to find a methodology that’s less transparent to work with, as cherished notions about story shape are seldom abandoned, with or without a fight.

There will be friction too

Developing a skill-set that requires that everyone pull together and collaborate on something complex creates friction. This is because everyone goes up the learning curve their own way and at their own speed. This can make small interpersonal difficulties that can show up in improv troupes suddenly seem to magnify and become horrible. It’s easy to believe that your little theater company is exploding, and sometimes they actually do. If you succeed, though, you’ll end up with a tightly-knit team of highly capable performers who can deliver work that’s as sharp as scripted theater.

Drills are your only real power-tools

Practicing frequently and relentlessly is the best way to develop V6H skills. Working on opening scenes until you can tell who the protagonist is going to be just by the way the first improviser walks on stage is going to make everything that follows that much easier. Drilling on nemesis scenes until you can assemble and propose an entire story premise in a single speech without once resorting to cliche takes time too. At the end of the day, good plays require a lot of non-declarative memory investment, and that takes time. Quite possibly, people will get bored at all the repeat play openings and complain. If that happens, do something else for a while. People only get better at improv when they’re motivated. Come back to the drills after you’ve tried a few more full-length plays and people have rediscovered how much they don’t know yet.

That's probably enough for now. Hopefully it's a useful start. For anyone out there playing with the V6H, I wish you the very best of luck. If you have any questions, I’m more than happy to address them. My your tension rise smoothly and your plays leave their audiences teetering, while laughing, on the edges of their seats.

Can Big (Companies) Be Beautiful?

2011-06-13T13:32:00.000-07:00

As companies grow, they slow down. Once they get past a certain size, they usually experience what’s called organizational sclerosis--a culture change that makes effective adaptation almost impossible. They cling to old business models, become less responsive to their customers, and make big costly mistakes. Their shares drop.

People have various theories as to why this happens. Most of them relate to organizational complexity or conflict from within. The strategies for fixing it usually involve hiring expensive consultants or laying off hundreds of employees. These strategies are painful and often simply don’t fix the core problem. But what if there was a way to make large companies as dynamic as small ones? I think I might have found one.

Part of the Tokenomics research that I’ve been doing of late has involved building computer simulations of organizational cultures. (See previous post.) The simulations are still very simple and there’s lots of work still to be done before any of the results find their way into a science paper. However, some of the results I've discovered are pretty compelling. This is one of them.

I’ve been exploring the idea that people look for predictable, repeatable self-validation in their interactions with others, or ‘token collection’--an idea that you can find out more about on the SF Behavior Lab website, or in the in-depth documents stored at Techneq.com. Almost all the token collection that we do is driven by non-conscious pattern-seeking. This is very useful because it means that we don’t have to model complete human minds in order to get a rough picture of how cultures work. We can simply create populations of agents that interact by seeking out repeatable patterns of dialog, and modifying their behavior if their conversations don’t run as expected.

In the examples I described in my earlier post, the agents building cultures had a pretty limited set of options available to them and so the communities they formed didn’t show much variation. However, for the experiments I want to share with you this time, the interactions are rather different. Agents speak in turns, rather than at the same time, and have a choice of 128 different words they can say. (128 openings x 128 responses). This means that the agents have a much harder time of it finding familiar subjects to talk about with their peers, and so have to be a little creative (random) in their choices.

Most of the interactions that the agents try out don’t meet with much success. They produce fairly short-lived patterns shared only by a few robotic acquaintances. However, some interaction patterns manage to spread and dominate, just like fads in real life. Once these patterns get established, they become incredibly stable. They become ‘cultural interactions’ and end up being common to all the agents in the simulation. Removing or adding agents incrementally doesn’t change the culture one bit.

Also, interestingly, changing the size of the population of agents has a serious effect on how often new patterns get incorporated into the culture. Bigger populations have a lot more trouble accepting new ideas than small ones. At some level, though, this shouldn’t surprise us. In order for an idea to develop critical mass and spread rapidly through the group, a certain percentage of the agents have to have already heard it. That’s going to get steadily less likely as the system scales up.

For these simple agents, a critical drop-off happens before you get to a population size of forty. So far as I know, there’s no equivalent change in performance for human groups in this size range. The literature suggests that real organizations start having problems at around a hundred and fifty people. However, in the context of agent simulations, this makes sense. The agents have much smaller memories than people and thus less intrinsic flexibility. So, while the drop off in effectiveness may not be exactly the same phenomenon as organizational sclerosis, there seems to be a close enough fit here to make exploring the effect worthwhile.

The question I asked was ‘what do I have to do to a set of agents in order to keep their rate of cultural adaptation high while the population increases in size’. Specifically, what did I need to do to make a population of forty agents as imaginative as one of size twenty.

The answer, for agents, at least, is simple. You divide them into two groups of size twenty and let them run independently. Then after a while, you reshuffle them into two new groups, and leave them alone again. Every time the two groups are shuffled, learning from each community gets transferred into the total population. However, because the groups stay small, the rate of creativity stays at what you’d expect for a much smaller population.

This process is rather like the genetic recombination that happens in sexual selection. We keep the rate of change high by randomly mixing our ingredients and then making sure that any useful results get shared back into the population.

Also, it turns out that this isn’t the only method that will work. Any solution that involves keeping group sizes small most of the time, combined with random mixing, will do the job. And there appears to be an optimum group size for a given setting of the simulation parameters at which creativity can spread fastest.

The lesson seems clear: turning big organizations into a cross-polinating swarms of smaller ones might make them a lot more agile. While this approach probably isn’t a fit for all kinds of business, I can think of plenty where it might work: finance and software being the first two to spring to mind.

Once I’d managed to keep a big culture as adaptable as a small one, I wanted to know if I could do better. After all, large organizations have more people and therefore more ideas per day than small ones. In an ideal world, shouldn’t they be able to capitalize on that?

The answer is yes if you bend the rules a little. I couldn’t find a way of reorganizing the teams that led to further improvements in creativity beyond what I’d already tried. However, when I started messing with the parameters of the simulation, I noticed something interesting: increasing the number of possible witnesses to each interaction by one caused the rate of creativity to jump up rapidly. One way to say this is that by raising the number of people in a team who get to listen in on important interactions increases the rate at which new ideas can spread

This provides some simulation evidence that the people in the Agile Software Development movement are on the right track. They’ve been saying for years that creating small, tightly-knit teams where information is regularly shared creates the right environment for creative work. Furthermore, the agents in this simulation are so simple and general that we can expect the same logic to hold for a large number of possible situations.

The important next step will be to see whether the same tricks work as I add in more of the complexity of real organizations. For instance, trust relationships, hierarchies, and differences in effective value between the habits being propagated all have yet to be added. If the splitting/merging effect still works when all these features are added in, it might be time to start rethinking how we grow companies. Organized packs of collaborative mammals could turn out to be a lot more adaptable than a few big dinosaurs.

Adventures in Game Theory, Part Four

2011-06-08T16:59:00.000-07:00

For those of you freshly joining this adventure, the last three posts have led us on a strange, thrilling journey that has passed through the valleys of introductory game theory, the jungles of applied improv, and the mountains of software simulation. Now, at last we arrive at our thunderous finale on the shores of Lake Awesome. I highly recommend reading from the start of the sequence, otherwise what I have to say may be too extraordinary and wonderful for your mind to fully hold!

At the end of the last installment caught me teetering on the brink of a realization--that by adding just a little more functionality to my simulation, I could start exploring some more socially useful truths about how people behave. My insight was to add status.

What this meant in practice was splitting the population of agents in my model into two groups: bosses and workers, or in training community parlance: leaders and team-members. Then, in order to make the interactions between bosses and workers a little less benign, I added two extra constraints.

One: If bosses were aggressive (nose-thumbing) to workers, workers were not empowered to reciprocate and be aggressive back in their next encounter.

Two: Bosses were unable to remember the specifics of positive interactions they had with workers. So for instance, if a boss and a worker both chose paper in one round, the worker would remember the fact, but the boss would not.

Implementing these changes was easy, as it simply required that the two memory rules I’d already added to make the first simulation work were now dependent on status. (I also added a little extra logic around the movement of the agents to ensure that workers had to interact with bosses, and to make the movements of bosses dependent on other bosses but not workers. However, while necessary, that code is somewhat beside the point.)

What happened next was wonderfully clear. Within a few seconds, all the bosses were behaving aggressively while the workers normed on a set of social standards of their own. My simulation suddenly looked a lot like some of the more awful companies I’d worked for. Without having to say anything about the kinds of people who become leaders, or about the specifics of organizational culture, I’d captured a simple truth about leadership: that without the incentives to behave otherwise and the right skills to succeed, people with power slide towards bad behavior, even if they start off thinking like saints.

What was even more interesting was that as the simulation progressed, the bosses started to bump up against the corners of the virtual environment as if desperate to leave. Because aggressive behavior was so successful for bosses in their interactions with workers, they were applying the same behavior to each other, resulting in a rapid erosion of their ability to collaborate. The lesson: by letting leaders behave badly, we ensure that leaders have less pleasant interactions with each other, as well as with us.

My goal, though, was not to engage in rhetoric about leaders, but instead to see whether models like the one I was looking at could tell us something about how to help organizations do better. To do this, I looked at what happened when I turned each of the status dependencies off in isolation.

Turning off the status dependency for remembering positive interactions is rather like sending your managers on an employee recognition course. They learn to value the specific information they get from each person they work with, and to let their team members know that they’re seen and valued.

The result in the simulation is that the culture improves significantly. The workers integrate more tightly and the bosses take on the same cultural colors as the workers they lead. Interestingly, the bosses don’t all start cooperating at once. Many of them initially retain their aggressive behavior. Then, one by one, they figure out that collaboration is more effective.

The lesson here: that training leaders to listen can make a huge difference in their effectiveness, but that the change they take on depends on their willingness to implement what they learn.

If instead, we turn off the status dependency for worker retaliation to boss aggression, the effects are even more interesting. Making this change is rather like implementing a shared accountability system like the one that revolutionized the airline industry and transformed the safety standards in air travel. Under this system, the pilots of planes are no longer the unquestionable captains of the air that they once were. If copilots think that they’re witnessing a mistake, they’re duty-bound to call the pilot on it and to report it to air traffic control if necessary. In our simulated business, we can imagine that we’re instructing the worker agents to hold their bosses accountable if they don’t uphold the collaborative social standards of their organization.

What happens when we make this change is that the behaviors of the bosses have trouble settling onto any specific color. When we watch the ‘mood’ of the agents to see how many positive or negative interactions they’re having, we see that the tables have been turned. The workers are now having a pretty great time all round and the bosses are mostly miserable--the opposite of what we see if status dependence for retaliation is left on. This is because the workers now have an advantage that the bosses don’t--they can remember and repeat positive interactions whereas bosses cannot. Because aggression no longer secures automatic results, bosses don’t have an easy way of stabilizing on a successful behavior.

The lesson here is that enabling everyone in an organization to hold leaders accountable for their behavior is what creates the incentive for leaders to improve, but that without the right training and direction, the main result is leader unhappiness.

As you might expect, turning off both status-dependent features creates a benign, functional organization that settles rapidly onto a cooperative culture. If you want to play around yourself, and have Java installed, the simulation is the second applet on this page. (It has four buttons.)

As before, red, blue and green denote different positive interactions. Gray denotes aggressive behavior. Swapping to ‘mood view’ shows the success of the agents interactions, ranging from blue (unhappy agents) to yellow (cheerful ones).

Clearly there’s a lot more to do here. For a start, in order to turn this into a science result, the simulations will need to be a lot more rigorous, which will probably mean sacrificing the visual playfulness. Furthermore, we’ve only looked at one memory model for agents and solid research would need to try out others. However, the results seem pretty clear. We’ve gone from a simple game played in a room full of people to a model that turns business intuition into something rather like unavoidable, mathematical fact.

Thus, in the wake of our adventure, we can say with real confidence that any society or organization that doesn’t empower its people hold its leaders accountable, and which doesn’t teach those leaders how to listen, can expect its leaders to turn out bad, regardless of how ‘good’ we believe them to be as people.

This is something most of us already believe but which we often fail to implement. For instance, we're all used to the idea of holding elected officials accountable, but explicit training in 'voter recognition'? We leave that to chance. Similarly, we're used to the idea that good managers are the ones who pay attention, but company-wide accountability systems? Those are pretty rare. I believe that simulations like this can make these points unavoidable, and also perhaps show us how to build measures that make our adherence to such standards quantifiable.

For any skeptics out there, my huge thanks for reading this far, and here’s a final thought to consider. Agent-based simulations of this sort have been used by biologists for years on the following basis: we can’t capture all the details of natural systems like cultures or the lives of organisms, so instead we capture only what we know is true. From that, we look to see what else must be true as a consequence. Thus we attempt to make the simplicity of the model a strength, not a weakness. In this instance, the agents are so simple that we can expect the same effects to arise regardless of the memory model we employ for our agents, so long as that memory model permits learning. Further work in this area will hopefully make that point even clearer.

That’s it. The adventure is finished. And while the ending perhaps isn’t unexpected, it feels like a step forwards to me. After all, if we can do this starting with Rock Paper Scissors, think what we can do with the game of Twister.

Adventures in Game Theory, Part Three

2011-06-07T15:25:00.000-07:00

To those fresh to this sequence of postings, let me give you a little context. Two posts ago, I implied that some kind of wildly significant insight about how organizations and societies worked could be derived from looking at simple playground games like Rock Paper Scissors. Over the course of the last two posts, I’ve been building up the case for that statement. Now comes the next thrilling, life-changing installment--this time with some simulation results!

Before I can fully explain, though, first I have to give you a little more background. Last week I had the good fortune to speak at the ASTD conference in Orlando, Florida, the world’s largest training and development business event. The topic of the session was the use of Tokenomics as a tool for organizational culture change. I delivered the talk with my good friend Cindy Ventrice, from MakeTheirDay.com, and to support the session we captured a large amount of material on the subject, which those interested can find on our collaboration website, techneq.com. The session went wonderfully and generated plenty of interest. However, what I’m most keen to talk about here doesn’t relate to that talk, exactly, but to the unexpected consequences of it.

In order demonstrate to the audience what the Tokenomics approach was capable of, I put together a short computer simulation based on Scissors Dilemma Party, a game which the readers of the last two posts will have already heard of. The simulation was designed to show how autonomous software agents, given nothing but a simple memory model and some behavioral rules based on token acquisition, would automatically aggregate into social groups defined by shared values.

To make the model more intuitively approachable for a conference audience, I chose to have the agents move around in a virtual environment rather like people in a workplace, interacting when they met. As well as making the simulation more visually appealing, it demonstrated how the agents’ behavior evolved over time as they learned more about their environment, much as players of the game do when they experience it at Behavior Lab.

Each agent had eight memory slots initially filled with random behaviors. With each interaction, an agent would pick a behavior from its memory and apply it. If the interaction resulted in a positive outcome for the agent (unreciprocated nose-thumbing, or a successful rock-paper-scissors match), that behavior was copied to another slot in memory. If the behavior resulted in any other outcome, that memory slot was overwritten with a new random behavior. Agents were designed to move towards other agents with whom they’d interacted positively, and away from those with whom interaction had failed.

At first, the simulation didn’t work very well. Aggressive behavior (nose-thumbing), was too seductive for the dim-witted agents and stable social groups never formed. In order to get the agents to behave a little more like people, I had to add a little extra subtlety. This came in the form of two new rules.

The first rule was that if an Agent A was aggressive to agent B, B would remember that fact and be aggressive back at the next opportunity. This captures the idea of ‘Tit for Tat’--a strategy that has proved very successful in Prisoner’s Dilemma tournaments.

The second rule was that if A and B had a successful match of rock, paper, or scissors, they’d both remember it and try for the same topic of conversation next time. This gave the agents a chance to reinforce positive relationships.

These two rules together did the trick and produced a somewhat mesmeric simulation. You can see it here, by just clicking on the first simulation button that appears. (Sadly, Blogger appears to become a trifle unstable when supporting applets, otherwise I would have included it in this blog. Also, note that you’ll need Java installed for this to work. If you don’t have Java, let me know. I’m thinking of writing an HTML5 version and am keen to know whether that would make life easier for people.) In this simulation, the colors red, green, and blue take the place of rock, paper and scissors. The color gray takes the place of nose-thumbing.

However, once I’d finished the simulation, it occurred to me that I’d only scratched the surface of what could be demonstrated with this approach. I could go further, do more, and start saying something really meaningful. Better still, the tools to achieve it were already in my hands! However, I’ve promised myself that each one of these postings will be short and readable by people with day jobs, so in order to discover what I did next, you’ll have to join me for Episode Four.

[Side note: my friend Cindy is awesome and so is her book. I can't recommend it highly enough.]

Adventures in Game Theory, Part Two

2011-06-05T09:51:00.000-07:00

In the previous installment of this adventure, I promised to reveal how the secrets to business effectiveness and social harmony could be achieved by playing games like Rock Paper Scissors. Will I be able to deliver on that outrageous promise? Only by reading on will you get to find out.

For the next part of our journey, let’s consider a new game which we’ll call Scissors Party. The rules are simple and very much like those of Rock Paper Scissors. Players bounce their fists as usual and then pick any one of the three gestures normally used in the game. However, the scoring system in this version is different. In Scissors Party, players get two points each if they successfully match their opponent’s choice and no points if they don’t match. So if two players both choose paper, they get two points each. If one player chooses scissors and the other chooses paper, nobody gets any points. As in Dilemma Party, players are free to stay with the same partner or mingle in the group as they like. Any guesses as to what happens?

You may have already guessed that players tend to form pairs and small clusters that make the same choice every time, eg: always rock or always paper. Even though lots of people will still mingle, they figure out fairly quickly that they’re not making as many points as the people who stay put. Just as in Dilemma Party, interpersonal dynamics add complexity to the game. Some people want to move around and take risks, while others just want to ace the game, so the results are never as perfectly consistent as we might imagine. However, the patterns are still pretty clear.

So far so good. But where it gets really interesting is when you put Dilemma Party and Scissors Party together. This gives you Scissors Dilemma Party: a game that gives players four options: rock, paper, scissors and nose-thumbing. The scoring works as you’d expect:

Thumbing gets you three points against rock, paper, or scissors but only one point against another thumb.
Successfully matching rock, paper, or scissors with your partner gets you two points.
Failing to match with rock, paper or scissors, or coming up against a thumb, gets you zero points.

Everyone confused yet?

What’s bizarre is what happens when you play this game with a room full of people who have just played Scissors Party moments before. Even though they know full well that they can form cliques and collaborate to get two points each turn, people will form little clusters that repeatedly thumb noses instead, getting one point each instead. This means that they’re being half as effective at playing as they were thirty seconds ago, simply because they’ve been given the option to play it safe at the cost of other players. This, to me, is a fascinating example of how being given the option to tune out and avoid cooperation produces instant defensiveness and a change in social cohesion.

Perhaps some of you will by now have figured out where I’m going with these games by now. Choosing different gestures in the game is very much like choosing tokens to collect in life. Pairwise interactions are rather like small versions of the conversations we have every day. Rock, paper and scissors equate to different forms of social value, such as sexiness, intelligence, or likability. Nose thumbing equates to extracting involuntary tokens from others for personal validation gain. Whereas our choice of gestures in the game is conscious and our choice of tokens in life is non-conscious, the same patterns of defensive behavior can be seen. In fact, in non-conscious group behavior, we tend toward more predictable responses. Thus, playing Scissors Dilemma Party gives us an interesting, lightweight model for looking at how social groups form and interact.

Intriguing, I hear you say, but still not yet a conclusive solution to the world’s ills. True. To see the awesome social significance of Scissors Dilemma Party in all its glory, you’ll have to read Adventures in Game Theory Part Three.

Adventures in Game Theory, Part One

2011-06-04T14:31:00.000-07:00

Question: Can playing simple games like Rock Paper Scissors teach us how to be better leaders, help us build effective, equitable organizations, and pave the way to a more harmonious world?
Answer: Yes! Undoubtedly!

If you want to know how, and why I would make such a ridiculous-sounding assertion, then I invite you to come with me on a journey into a dark and mysterious world of theoretical applied improv. The journey will be long and arduous (four blog posts), but for those who stick with me, there is treasure in store.

The starting point in this adventure is the Prisoner’s Dilemma--perhaps the best-known finding from Game Theory: a branch of math that studies how people or animals compete. Simply put, the Prisoner’s Dilemma is a formal description of a kind of situation we often face in life, in which cooperation between two parties comes with both risks and benefits, but where failing to cooperate is both safe and predictable.

People have studies Prisoner’s Dilemma very extensively. There have been research papers about it, world-spanning experiments, online tournaments between competing software programs, and dozens of books on the subject. Not satisfied by all this, I wanted to see what happened when I turned Prisoner’s Dilemma into an improv game and took it to Behavior Lab.

To this end, I created a game called Dilemma Party--a little like Rock Paper Scissors but with two options per player instead of the traditional three. Here’s a slide I used at the ASTD conference in Orlando recently (more on that in later posts), that shows how to play, and how the scoring works.

As you can see, players have the option of thumbing their nose at their opponent or offering them an invisible gift. Offering a gift presents the best opportunity for mutual gain but comes with a risk. If the other player thumbs their nose at you, you get nothing and your opponent walks away with a nice stack of points. Thumbing your nose means that you always win something, regardless of what the other player does--it’s a safer bet but not a particularly friendly one.

Players of the game interact for an unspecified period of time, trying to rack up as many points as they can. They’re milling in a large group and can swap partners any time they like, or stay with their current partner if they prefer. What do you suppose happens if you put fifty random people in a room together and get them to play? Any guesses on what strategies they pick?

The answer is that it depends on the group. Put members of the general public together and the group norms to almost universally thumbing noses after a short time, with a few individuals doggedly giving gifts regardless of the losses they incur. However, put a room full of professional trainers together and the group norms to universal gift giving almost as fast. Perhaps unsurprisingly, pairs of players who settle on gift-giving tend to stay together. Pairs where one or more players thumb noses don’t stay together very long.

For the most part, people who aren’t already familiar with the Prisoner’s Dilemma do a very natural thing when reasoning about scores. They realize that by nose-thumbing, they can’t lose, so they keep doing it, even though they miss out on the chance to make more points by building stable relationships. No big surprises there.

Where the game gets interesting is when you look at how the rich, multi-layered nature of human interaction interferes with our stable assumptions about how the game should work. For instance, in one group, players repeatedly thumbed their opponents but then shared high-fives after each interaction. What this suggests is that the players knew they were making cautious, uncooperative choices, but still wanted to check in with each other to show that they were really friendly people at heart. Thumbing their noses felt awkward and antisocial but they didn’t want to change tactics and consequently lose! Giving high-fives was a way of subverting the game, and showing their opponents that they weren’t really in competition.

Also, those people who’ve spent a lot of time in a training, group therapy, or social workshop setting tend to repeatedly offer gifts, regardless of the consequences. I suspect that this has more to do with how those people are mentally parsing the game, rather than suggesting that they have fundamentally different personalities. These are people who’ve played similar games before and aware of the implications of cooperation. That makes them behave differently because perceiving themselves as cooperative affords them more validation than the points offered by the game. They’d rather feel positive and socially useful than win, even if that feeling comes with a very light dose of martyrdom.

Underpinning both of these reactions is the fascinating interplay between the choices made consciously in the game, and the very similar game of token exchange that the players are playing underneath. Because we load the game into the conscious awareness of the players, the acquisition of points can’t help but be held as an extrinsic goal. And because there aren’t cash prizes on offer, that goal comes with low priority. This means that the intrinsic motivations of the players guide their strategies. Thus, while we’re unlikely to get unbiased information about Prisoner’s Dilemma itself from the game, it shines a fascinating light on our motivations.

Interesting, I think, but not a recipe for social harmony just yet. There’s more we can do with these games. Much more. And for that, you’ll have to read my Adventures in Game Theory Part Two.

The Science Incubator Game

2011-02-07T09:54:00.000-08:00

Science is fracturing. People from different fields don't really understand each other's work all that well. Specialty areas keep getting smaller and more focused. Furthermore, many scientists have to operate in a culture that discourages people from opening their mouths if they don't understand what's being said. This is because how 'brilliant' others imagine you to be often has immediate repercussions for the job you get next. This unwillingness to speak up only makes the fracturing happen faster.

I can't see this culture of caution ending any time soon without outside help because it's driven by two things:

It's just harder to understand what people in other fields are doing these days because the amount of understanding that you have to invest to reach the coal-face of science is hugely more than it used to be. Consequently, people try less.
The pressure in the scientific job market is incredible and it's getting worse. Gone are the days when people walked straight from their PhDs to faculty jobs. The incentives for people to open their mouths and risk looking foolish have never been lower.

This whole trend is unfortunate, because the research shows that interdisciplinary dialog accelerates progress. Groups with mixed skill sets consistently find solutions faster than teams of people who specialize in the same subfield. The act of having to articulate your ideas to those who may not understand is not only going to force you to bring order to your own ideas, but is also likely to lead to the creation of new ones. Creativity, as it turns out, is not driven by sudden sparks of spontaneous genius, but by a process of blending pre-existing notions. Whether this happens inside a single person's head or in a social context doesn't seem to matter.

Furthermore, social innovation is always best activated by play and many of today's scientific workplaces are still lamentably low on playfullness. Whereas software companies have incorporated all manner of tools for establishing a sense of fun into their offices, many scientific departments still imagine that it's somehow 'more professional' to have people sitting in silence in small offices.

So, is there something we can do to fix this? Can we use applied improv to make science healthier, smarter, and more playful? I think so, and here's my best guess so far as to how to do it.

The trick is to play the science incubator game. For this you need:

A cafe.
A volunteer to be master of ceremonies.
About six people who like to learn and think.

The science incubator game takes the form of an open, flowing dialog. An ideal session is likely to last from one to two hours, depending on everyone's stamina, and works like this.

One person, the 'proposer' brings along an open question that they're trying to answer. This can be as abstruse and as deep into their work as they like. In fact, the more abstruse, the better. The proposer tries to explain their problem to the rest of the group.
The others ask questions every time what they're hearing isn't clear, and chuck in any ideas that seem relevant. Everyone else in the group to tries to learn, and be as supportive as possible.
The master of ceremonies acts as an adjudicator, making sure that everyone gets a voice and that the rules are followed.
The session should start with everyone in the group telling a deliberate lie, so as to activate the creative parts of their brains. The more confusing or elaborate the lies are, the better.

The rules of the game are:

All questions and ideas are good. Nobody gets to pass judgement on anyone's question or idea, regardless of how flawed they think it is. The proposer should try to answer all questions that are asked. (Why: Uses the Yes-And principle to create a shared narrative.)
Interruption should be done politely, but is mandatory. If the dialog has gone on for five minutes without somebody chiming in with a thought or suggestion, the master of ceremonies asks a question of his own. (Why: Prevents grandstanding and encourages group ownership of the process.)
Silence is banned. If a silence lasts for more than five seconds, the master of ceremonies should chime in with a new question. (Why: To maintain the energy level.)
Everyone is equal. All work hierarchy is left at the door when the incubator game is in progress. Anyone who pulls rank, or attempts to refer to their depth of experience to validate a point gets an immediate reprimand from the master of ceremonies. (Why: To help the space feel safe to all, and removed from normal patterns of social cost.)
Negativity is banned. In academic settings, people often consider their value to be in filtering out the proposals that won't work. In the incubator game, this role is forbidden. The way to add value is to add more ideas. (Why: Prevents contributors from self-awarding value via 'critical rationality' and derailing the session at the same time.)
Everything is informal. The purpose of the incubator is to reduce the risks of making suggestions and asking questions. Everyone is there to learn. Humor is strongly encouraged. (Why: Laughter activates the signal for social learning.)
Everyone must contribute. If someone has been quiet, or has been left out of the process, it's the master of ceremonies's job to bring them back in and make sure they feel safe. (Why: To encourage acceptance of the 'price of entry' of the session, which is engagement.)
Everyone should try to insert at least one harebrained suggestion that they have just thought of without considering the implications properly. Anyone who confesses 'I have no idea what I'm talking about' should get an immediate cheer. (Why: To break the dangerous social habit of over-filtering ideas out of perceived risk.)
The dialog stays on topic until the session is over, and shouldn't deviate into gossip. (Why: To break the idea that 'shop talk' is somehow dull, and to create as much engagement in new ideas as possible.)
Nobody is 'on show'. If the master of ceremonies feels that the game is dissolving into a performance of sorts between a few people in the group, or if people are waiting to have a 'good idea' before chiming in, the master of ceremonies needs to fix the balance. If necessary, the master of ceremonies can ask a specific person in the group for 'a half-baked idea, please'. (Why: To prevent social dominance patterns from forming within the game.)

Variations:

This game doesn't have to be played in a cafe, of course, but ground that feels neutral and safe is a good idea. At someone's house over a shared pizza would work equally well. A glass of wine might also assist the process.
Also, it clearly doesn't need to be done by scientists, either. I suspect that any group of people who're looking to share ideas and brainstorm would probably benefit from something like this.
Leaving out rules that aren't working, and letting the proces flex to reflect the needs of the group is a good idea, so long as the spirit of the rules is maintained.

Ways to make the process stronger:

If everyone brings pencil and paper, and tries building pictures or mind-maps of what they're hearing while others are speaking, it's likely to broaden thinking and keep the energy level up. (Why: Using the visual parts of the brain is likely to increase engagement.)
Courageous, committed scientists should wear unusual hats, or pin large, ridiculous flowers to their lapels, or some such thing. Any symbol that creates a sense of group inclusion and of willingness to be silly is commended. (Why: Anything which fosters laughter is likely to improve the quality of social learning, and the bond that's formed.)
If a proposer focuses on recent work they've done which went badly wrong, and talks over what happened with positive input from others, this is likely to help boost the proposer's learning experience significantly. (Why: Research shows that negative reinforcement without emotional consequences maximizes learning rates.)
Wind down time after the game is probably a good idea. If people get to keep talking and laughing after the end of the session, it's likely to improve the sense of group connection. (Why: The game is likely to encourage intensity. People will feel the need to re-establish working norms afterward.)

I've no idea whether this game works or not because I haven't tried it yet. To some extent, this game simply outsources the difficulties of scientific collaboration to the master of ceremonies--a kind of magical mediator who requires the courage and wisdom to step in every time the dialog falls off track. However, finding such a mediator might not be so hard. The role that I've outlined here is something that every decent improv instructor knows how to do. The necessary skill sets are surely easy to find if we go looking for them in our communities or take the time to acquire them for ourselves.

I'll be running some experiments here in Berkeley if I can find enough brave scientists. If anyone has any suggestions on the format, or gets to try it out before I do, I'd be delighted to hear about it.

TED Talks I Like

2011-02-01T10:25:00.000-08:00

I like TED talks. They’re a marvelous way of getting access to many fascinating ideas in a very short time. (They’re also a fascinating series of examples of what does and does not make for compelling public speaking, but that’s a whole other blog entry.) At the suggestion of my good friend at SFBehaviorLab, David Sals, I’ve put together a list of some of the talks I like.

First, here are some talks by authors I’ve already raved about in my recent reading list post. These people therefore need no introduction.

And here are are a few that I’ve recently encountered, all of which I think deserve a viewing.

Charles Limb

This has to be a great place to start. Clearer evidence of the profound neurological effect of improvisation would be hard to find.

Laurie Santos

This is a great one to watch after the Dan Ariely talk listed above. Laurie Santos clearly demonstrates that the kind of decision-making ‘mistakes’ we make aren’t specific to the human race. This suggests that these patterns of reasoning are old. To my mind, this has important implications.

The patterns of decision-making that behavioral economics has revealed don’t just tell us things about how people react. They’re very likely to be providing us with important insights about how effective reasoning works. These ‘mistakes’ have been selected for over the course of millions of years of evolution. If they cause us to make some choices ineffectively, there must be other advantages that we gain. Though it may not be clear yet exactly what those gains are, experiments in Machine Learning are likely to help us find out.

Daniel Kahneman

What I like about this talk is the distinction that Prof. Kahneman makes between the way we experience things and the way we remember them. He points out that the connection between the two is far shakier than we’d like to imagine. For me, this says interesting things both about the nature of declarative memory, and how we can use it to make our interactions with each other better. For instance, it seems clear that following negative feedback to a person with something a little nicer is likely to cause that person to walk away with a far rosier impression of the experience than if only negative input is received. This suggests that fixing some toxic workplace interactions may be as simple as bolting positive rituals onto the end of them--a fascinating implication, if it’s right.

Nicholas Christakis

This talk is on how we can use an understanding of social networks to gain insights about the spread of diseases, social trends, and even emotions. Most significantly, Prof. Christakis reveals a simple mechanism by which we can identify ‘hubs’ in social networks and use them to gain advance warning of changes sweeping through a population. However, he also shows that interacting with these hubs provides us with a way to intervene as well as to watch. For instance, it tells us how to best to deploy a vaccine into a population to save the maximum number of lives.

The implication for applied improv here is that the same tools enable us to find those members of a community most likely to A: reflect the values of a culture, and B: change them, if we can engage them and give them the right tools.

Steven Johnson

Mr. Johnson’s research into the the kind of social environments that foster good ideas feel like a natural fit for applied improv. Lurking in here, I feel, are clues as to how to use the science of play and the study of behavioral games to create innovation incubators. This talk leads me to wonder what kinds of improv you can play sitting down with four molecular biologists in a Starbucks without having anyone raise their voice or leave their seat. My suspicion is that one can do quite a lot, and probably get some publication worthy material out of it at the same time.

Books I Like

2011-01-24T10:01:00.000-08:00

On my literary travels last year, I came across lots of books that helped me build a stronger understanding of how improv works in the brain. Not all of them look relevant to applied improv at first sight, so I decided to put together a short reading list of a few of my favorites for anyone interested in exploring the same topics. It's my hope to expand this list in future posts.

Influence: The Psychology of Persuasion (Collins Business Essentials)
Robert Cialdini
An astonishing book. In essence, it’s a guide to the operating system of human social behavior. Cialdini reveals ways that human beings run on automatic while trying to get along and shows how those behaviors are routinely exploited by the unscrupulous. This book is invaluable for anyone interested in not being manipulated by others, but is also incredibly useful from an improv standpoint. The chapter about the ‘authority principle’ is essentially a lesson on Status. However, there’s a lot more in here that improv hasn’t explored as deeply. The ‘reciprocity principle’, for instance, has a lot in common with ‘make your partner look good’ but seems to go deeper. The research that Cialdini recounts suggest a wealth of possible games that have yet to be explored.

Influencer: The Power to Change Anything
Patterson, Grenny, Maxfield, and McMillan
The best book I’ve read so far on enabling social change. Simultaneously readable and scholarly--this book encourages a data-driven approach to understanding organizational culture, and doesn’t pull punches about just how hard it can be to make a lasting difference. It outlines a clear plan of the steps that leaders need to take if they really want to mend the communities they work in.
There’s plenty here too for applied improvisers who don’t happen to be working directly with business. ‘Influencer’ provides a useful guide to the effect of human motivations on group behavior and draws on real life examples like drug rehabilitation programs and disease prevention projects in the developing world.

Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions
Dan Ariely
An approachable, clearly written book on the growing field of behavioral economics. While there are other books out there that also do a good job at introducing this material (eg: Sway by the Brafman brothers), Predictably Irrational lays out each important result in a clear and concise way.
For those out there who haven’t looked into behavioral economics, I highly recommend exploring it. It sheds a great deal of light on many quirks in the human decision making process--such as why we like free gifts so much, and how the credit crunch happened. This new field is rife with experiments that cry out for adaptation into improv games.

On Intelligence
Jeff Hawkins
This is a book on how the human neocortex works. Mr Hawkins wants to duplicate the learning system it employs and use it in software to create intelligent machines. His company, Numenta, is making great headway in this department, and has already developed software for motion detection and fraud analysis based on insights from biology.
At first sight, this might not seem like a book for applied improv enthusiasts, but in fact it was one of the most important books I read last year. It makes it very clear exactly what the brain does that’s so special, and how human learning actually works. Locked in here is the secret of why ‘I suck and I love to fail’ is such an important concept.

Drive: The Surprising Truth About What Motivates Us
Dan Pink
A friendly, highly digestible account of motivation theory research. While this book has some things in common with Influencer, its thrust is more inspirational in tone. Dan Pink shows how the principles of Autonomy, Mastery, and Purpose broadly define what people want out of life, and how some of the most enlightened business leaders in the world have been able to put those ideas to work.
In essence, the book is an appeal to managers to stop thinking in terms of cash incentives and old-fashioned economics, and to use modern psychology instead. Gratifyingly, his message lines up tightly with the kind of motivational wisdom that improvisers have been using for a long time. There are plenty of examples that trainers can grab hold of and apply directly with their clients.

Play: How It Shapes the Brain, Opens the Imagination, and Invigorates the Soul
Stuart Brown
This book is a must for applied improv enthusiasts. It lays out research that shows how the act of playing activates the oldest, most highly evolved system for learning that human beings have. The message is clear: training that doesn’t incorporate play isn’t really training. Sure, it might be informative, and even slightly useful, but nothing enables soft skill acquisition like the collaborative social experimentation that’s signaled by laughter.
This book very successfully explodes the myth that play is somehow trivial and that real business is serious, and reveals that the reverse is usually true. Effective businesses, communities and institutions leave room for play and laughter, while trying to be ‘serious’ tends to lead to impaired decision-making.

Open Tokenomics Event--Everyone Invited

2011-01-20T09:51:00.000-08:00

Tuesday, Feb. 15th, 6-8pm

1590 Bryant St., San Francisco

This is a free event

Good news for everyone out there interested in experimental applied improv, and particularly for those who happen to live in the Bay Area. The SF Behavior Lab project will be holding its first public open event in San Francisco next month--on Feb 15th to be precise. The event is open to all, and will happen from 6pm to 8pm. Afterward, we’ll be going to grab some dinner in the neighborhood and you’re welcome to join us for that too.

The evening will require no improv training and will hopefully have something to offer both interested laypeople and experienced improv trainers looking for new workshop ideas. There’ll be an opportunity for improv professionals to share experimental games in a safe setting, and also plenty of new games to try out. While we build up steam, we'll be keeping the Behavior Lab events free, so make sure you invite anyone you know who’s looking for a fun evening out.

The focus of this first session will be on Tokenomics--the topic I gave a talk on at the world applied improv conference in Amsterdam this year. When I spoke again at the Bay Area mini-conference in December, we didn’t have enough time to explore games that addressed this topic. Next month’s session will hopefully address that imbalance, and give everyone in the area a chance to try the material out for themselves.

If you don’t yet have a clear idea of what Tokenomics is or what I’m talking about, the good news is that you can now find out through the Behavior Lab website. This talk is a little rushed (I was trying to squash about 45 mins worth of slides into about 25 mins) but will give you a sense of what’s in store. It should be a very entertaining evening, and hopefully highly informative too.

The Feb. 15th event will be held at Sports Basement, at 1590 Bryant St. in San Francisco. Sports Basement has generously offered a 20% store-wide discount that evening for anyone connected with SF Behavior Lab, so be sure to come early and browse the aisles.

PS: For those of you who have already signed up at the Behavior Lab website, please take another look and check that your membership still exists. The site encountered some minor teething troubles at the end of last year, and some attempted sign-ups were lost. (We want everyone who wants to play with us to get to!)

Hope to see you all there!

I'm Back

2010-12-09T18:47:00.000-08:00

Hello blog readers!
I have been away having numerous exciting adventures and am now back in improv land. Among my travels, I got to attend the AIN International conference in Amsterdam in September, which was a wonderful experience. The speakers were fantastic, the energy was incredible, and the aura of sheer utopian functionalness that came from having so many applied improvisers in the room at the same time needed to be experienced to be believed.

I was lucky enough to give a talk, and got to share some of the ‘science of improv’ content I’ve mentioned on this site previously. For those who’re interested, the slides can be found here.

Tomorrow, I’ll be giving the same talk at the Bay Area AIN mini-conference in San Francisco, which should also be a lot of fun.

Since getting back from Europe, I’ve been thinking hard about how to close the gap between improv training and the behavioral sciences and things are starting to come together in an interesting way. So, it’s with great pleasure that I’d like to announce the opening of a brand new website, the SF Behavior Lab:
http://www.sfbehaviorlab.com/

This site is intended as a local rallying point for those people in the Bay Area who’d like to get involved in collaborative events aimed at understanding human interaction through play. The site will include results from software simulations as well as live workshops (more about that in later posts), and hopefully also interactive virtual improv games.

If it sounds like fun, you’re right. I hope to see you all virtually there.

Employee Recognition and Cleaner Fish

2010-06-14T16:27:00.000-07:00

My good friend Cindy Ventrice and I have been working for the last month or two on kicking off a rather exciting new training program. To help launch this venture, I wrote an article for Cindy's blog on the reciprocity principle and it's use in the workplace. I'm pleased with it. Anyone interested in the similarities between good management practice and swimming around in the mouths of large carnivorous fish should take a look. You can find it here.

The Science of Improv Part 1: How to Fail Magnificently

2010-06-08T17:58:00.000-07:00

Why does improv change lives? Why are improvisers usually more relaxed and more open than the other people I meet? Why are improv instructors the most engaging teachers I have ever known? How come people who improvise seem to make better doctors, scientists, salespeople, leaders, writers, and engineers than their counterparts? Why do improv class attendees keep coming back over and over again as if for doses of some kind of magical drug?

I’ve spent the last five months conducting research propelled by the belief that these questions can be answered, and that the answers will be rooted in solid science. Finally, a clear picture is starting to emerge. This picture binds together thinking I’ve encountered from motivation theory, behavioral economics, emotional intelligence research, neuroscience, machine learning theory, and psychoanalysis. Explaining all that I’ve learned would take a book and I haven’t finished putting the pieces together yet. However, I’m going to start trying to explain what I’ve discovered. I have a bunch of thoughts to share with you, so I’m going to break the journey up into several posts. Hopefully, it’ll prove both useful and entertaining.

I Suck and I Love to Fail!
One of the greatest lessons that improv teaches is that if you don’t trust yourself to do something perfectly, do it with glee instead. Great improv teachers like Kat Koppett talk about ‘celebrating failure’. Troupes all across the world yell out ‘I suck and I love to fail!’ to each other with huge grins on their faces before going onstage. Trainers everywhere talk about ‘mistakes as gifts’. It all amounts to the same thing: making the most out of getting things wrong.

This idea might sound cute and more useful in a comedy show than the workplace, but I’m going to show you how it can help us overcome arguably the most fundamental limitation of the human mind. I’ll show you how it can change both the way we think, and what we’re capable of achieving.

The Brains of Bugs
In order to explain, let’s cover a little background about how thinking works. And, for starters, let’s look at the most basic kind of thinking we know about: the sort of thinking that happens in simple creatures like bugs and slugs. Unlike us, these creatures don’t spend much time worrying about bank balances or planning vacations. Instead, they just react to the stimuli they sense around them. It turns out they are capable of learning, but only in the most basic way possible: by associating certain stimuli with either pleasure or pain so that they know whether to seek them out or avoid them in the future.

AI researchers have spent plenty of time studying the brains of these kinds of creatures and it turns out that the root principle of how they work is pretty simple. Each neuron in a simple brain can be thought of as a bit like a voter taking part in an election.

Let’s imagine a contest a bit like American Idol. In this contest, all the viewers are watching over the internet and the footage that each voter sees is a bit different. With each round, the voters are all shown footage of the contestants, and on the basis of what they see, they vote. The votes are tallied up and one of the contestant wins.

However, what the voters in our contest don’t know is that their choices have big implications. Lucrative music industry deals are being won or lost on the basis of who gets picked. So after each vote, the entertainment company that hosts the contest changes the footage that each voter will get to see in the next round by just a little bit, so as to make sure that voting in future will be a bit more favorable to their interests. Those who voted for the ‘right’ candidate get to see more footage. Those who voted for the wrong one only get to see some edited highlights.

In a real insect’s brain, instead of singers to choose between, we have decisions like ‘remain very still’ or ‘flee from the spider’. Instead of an entertainment company we have the consequences of those decisions, such as ‘going unnoticed’ or ‘losing a leg’.

After each pleasant experience that our bug has, the voting neurons that caused him to have that experience get reinforced--they get to see more footage. Those that tried to guide him away get weakened--they get the edited highlights. It works the same way when our bug experiences pain. Those neurons that cause him to walk into an unpleasant experience get weakened. Those what would have helped him make a different choice get reinforced. In this way, and with a little luck, our bug learns to make better decisions over time. With luck he gets to breed before turning into somebody’s snack.

While this picture is, of course, a sweeping generalization about how simple brains work, hopefully it makes it clear that pleasure and pain use similar mechanisms. In both cases, some neurons have their connections strengthened while others are weakened. So far so good, but human brains aren’t like the brains of bugs. People, for the most part, are smarter.

The Human Difference
There are lots of differences that we could talk about between the brains of bugs and those of people. It’d be easy to get caught up in a conversation about consciousness, for instance, or creativity. But the broad, distinguishing difference, I would argue, between simple brains and brains like ours is that our brains can make plans.

Somehow, we’re capable of behaviors which don’t head us directly towards pleasure or away from pain, even though, at the end of the day, we like pleasure at least as much as your average insect. We’re even able to devise goals like visiting the moon. In order to achieve such goals we have to be able to build tools, cooperate, imagine, and reason out enormous problems, all without any kind of direct sensory reward, like, say, a large quantity of chocolate cake. How does the brain do it? I propose that the planning process in our brains happens by a process we might call recursive goal matching.

Recursive goal matching means that when a person is considering some reward he’d like to get, his mind identifies interim scenarios that will help him reach that reward. It then treats those interim scenarios as goals in their own right and tries to build chains of behavior that will reach those goals. Some of those chains of behavior contain interim scenarios that become new goals, and so on.

For instance, in order to get the payoff of eating the cake we’re keeping in the fridge, first we have to go to the kitchen. Visiting the kitchen is an interim scenario that becomes a goal. There’s no intrinsic reward in getting there, but in doing so, we’re helping fulfill our yummy plan. However, visiting the kitchen requires that we leave the couch and walk there. The goal of leaving the couch requires that we turn on the table lamp beside us so we can see our way. Etc.

When we successfully complete a goal scenario we’ve pictured in our mind, we receive a small jolt of internalized satisfaction. If we fail to match such a scenario after repeated attempts, such as for instance discovering that someone has locked the kitchen door to keep us away from the cake, the mind sends us a dose of frustration. Just as in the case of the insect brain we looked at earlier, our brains manage our behavior with tiny pulses of neurotransmitters that change how our voting neurons are wired, and thus, what they get to ‘see’.

For a fascinating account of how the brain constructs behavior out of hierarchies of such plans, I recommend ‘On Intelligence’ by Jeff Hawkins. However, what no book I’ve read so far has yet pointed out, though, is that the process of learning through planned behavior is asymmetrical. In other words, while achievement and pleasure have plenty in common, frustration isn’t experienced like pain.

The Magic of Failure
Just as in the case of a pleasure response, the satisfaction we get from reaching a goal reinforces those neuron connections that enabled us to get there, while weakening those that voted for other options that would have prevented us from succeeding. However, what happens in the case of frustration? When we fail to reach a goal, we have no idea which neurons to reinforce because we lack the knowledge of what would have caused our plan to succeed. My guess is that the brain has no choice but to weaken all the connections that might have been responsible. This means that unlike pain, failure is a universally negative sensation.

Let’s go over this again to make it clear. We’ll look at each emotion and the effect in the brain that it causes.

Pleasure: Strengthen connections that led to pleasure. Weaken connections to neurons who voted against it.
Pain: Weaken those connections that led to pain. Strengthen connections to neurons who voted against it.
Achievement: Strengthen the connections that led to us reaching our goal. Weaken those that voted against it.
Failure: Weaken those connections that led us down the path to the failed goal. Wait, what do I strengthen? We don’t know which neurons voted against failure, because any part of our plan might be responsible for it not working! If we get to strengthen anything, its the neurons that voted for us to not take this goal on in the first place!

The Frontiers of Science
Here’s an example of this principle in action. It happened to a good friend of mine. A scientist we’ll call Amy. Amy recently had a wild idea that she thought was going to change her field. She spoke to her boss about it, but her boss shrugged and pretty much dismissed the idea as impossible. So, undeterred, Amy boldly set about exploring the idea on her own.

To her great delight, it worked. Amy was terribly excited, and convinced that her discovery would make her career. She felt on top of the world, and was rightly proud of her achievement. When she carried out a literature search, she could find no evidence that anyone had uncovered the same extraordinary result. She wrote up the paper and submitted it. Then, out of paranoia because the stakes were so high, she conducted a second literature search the moment the paper was accepted. This second search turned up a paper that had uncovered the same result three years earlier! Amy hadn’t found that paper because no-one in the literature had been referencing it. She was mortified and withdrew her paper from the journal the same day.

When I learned all this from Amy, I could tell that her self-esteem had taken a hit. She spent the evening questioning whether she was in the right field and whether she was cut out for science at all. She felt like a failure, and stupid for having made a mistake. I’m sure we can all understand how she felt. But let’s take a closer look at what happened.

For starters, Amy’s result was right. The fact that she wasn’t first to find it didn’t affect that. Furthermore, it wasn’t surprising that she didn’t know the result had been found before because nobody had referenced the work. This was simply because the result wasn’t one that anyone wanted to be true. There’s a whole other blog post in here that I’ll save for another time, but broadly speaking, even in science, people see what they want to see.

Despite the fact that Amy was now one of only two people in the world to understand a deeply important result, Amy felt like a loser. This was because the goal she’d built in her head was that of publishing a paper and getting recognition for the result. Her brain had automatically matched to the end result of the process she’d anticipated, without considering the interim benefits like ‘doing good science’. Furthermore, her instinctive response to this crisis wasn’t ‘I need to do better literature searches’, but instead simply, ‘I failed’. She went from contemplating her own genius to wondering if she needed to look for a new job, all inside of about half an hour. To my knowledge, Amy still hasn’t really pursued her line of research further. She feels kind of bad about it. Being brilliant hasn’t prevented Amy from having a brain that doesn’t know how to process failure.

I strongly suspect that if we look back over our own life experiences carefully, we’ll spot situations we’ve all been in that are very much like Amy’s. I know I have. Such episodes can be hard to pick out because the brain doesn’t like to think about failure, but they’re there. In my experience, it’s often easier to catch such experiences while they’re happening. Next time you experience a sudden surge of self-criticism, ask yourself exactly what the goal is that you’re not matching, and whether it even makes sense.

Loss Aversion
So, in a nutshell, when we succeed, we get reward for a specific result. When we don’t match a goal, we get blasted for everything we’ve done recently, even if the goal we were matching against wasn’t a very meaningful one. This effect changes the kind of plans we’re likely to come up with. We’re going to be biased toward those plans that avoid failure, because failure experiences are going to be disproportionally negative.

Sure we can all ‘learn from failure’ as self-help books encourage us to do, but those very same books have to encourage us to do it because it’s not a natural part of our thinking process. Without conscious coaching, the brain usually has no idea exactly what the lesson is that each particular failure grants us.

The fact that we treat failure differently from pain can help explain why people react irrationally to the presence of free gifts as Dan Ariely describes in Predictably Irrational. Something that’s free comes without mental attachment to costs, and therefore potential failure scenarios, which makes it automatically desirable in plan building. It also explains the principle of 'loss aversion', as described in Sway by the Brafman brothers, that causes people to sometimes go to seemingly absurd extremes to avoid failure.

Is there any evidence to support the idea that this difference in learning patterns is responsible for these effects? So far, the evidence is still thin, but it’s building. Recent research has revealed that damage to the amygdala causes loss aversion to be suppressed. The amygdala is the part of the brain that’s responsible for dealing with the consequences of fear and other similar sensations. If the phenomenon of loss aversion is bound up with the process of suppressing links between neurons, just as pain is, then this is exactly where we’d hope to find the experience centered. This result is far from conclusive, but it’s a start. However, while this result is interesting, it still doesn’t say much about improv. For that, we have to look at some of the implications of how the brain processes failure.

Emotional Intelligence
When the brain knows that it’s failed, but not why, it has a problem. Just like any general waging a campaign, it has no choice but to invoke ‘Plan B’. Plan B, in this case, is a suite of backup behaviors designed to resolve tricky situations. These behaviors are ones with a long track record of proven evolutionary success and broad applicability. However, they’re often significantly less sophisticated than the behaviors we build via planning. Our backup behaviors are designed to get us out of trouble fast and often come with physiological knock-on effects to accelerate our responses. These behaviors are ones we might class as ‘irrational’. Which behaviors are kicked off depends on how much stress we’re already under when our plans start to fail.

This is why people become irrational when negotiations fail or expectations aren’t met. It underpins the sort of situations outlined in Crucial Conversations by Patterson et al, and connects up tidily with the themes of emotional intelligence research pioneered by Dan Goleman.

What we know from this research is that by changing how we look at failure, we can change how we respond to it. Our subconscious mind isn’t so hot at deciding what kinds of failure are genuinely dangerous, because failure, by definition, represents a lack of data. However, with a little conscious reflection it’s often straightforward to see that we’re being intuitively navigated away from situations that just aren’t that risky. Consequently, exercises that encourage the brain to treat failure as something to be accommodated and embraced mean that our more extreme ‘Plan B’ responses get activated far less frequently. By habituating a reasoned, conscious, up-beat response to failure, we stand a far better chance of coping well when something goes wrong, and this is what makes improv exercises so powerful.

By remembering to say ‘I suck and I love to fail!’ we are directly targeting and deactivating that part of ourselves that gives us stage-fright, makes us panicky in romantic situations, or gets us into fights. The more we practice that response, the easier it gets. This is what makes improvisers look so witty and fearless on stage. They look that way because they are witty and fearless. However, this isn’t because some kind of innate talent. It’s because their brains have learned to treat being on stage as exactly what it really is: just being at the other end of a room from a bunch of people sitting down.

I suspect that at the scale of whole societies, some well-applied improv exercises might go a long way toward making the world a more peaceful, rational place. Not a bad result for a trick to take the edge off unplanned performances. Admittedly, though, I haven’t said anything yet about what shape our mental plans actually take or how we choose which ones to follow. I’ll cover that next time, and that’s where it starts to get really interesting.

An Intro to Archetypal Improv

2010-04-16T18:13:00.000-07:00

On Sunday, I ran an introductory workshop on Archetypal Improv for the excellent SFImprov group and was delighted at how it went. We had a really nice turnout and managed to get through loads of material. Several people asked me, though, if I’d be supplying notes afterward, so that they could go back over what they’d learned. Here then, in the spirit of sharing, is a brief outline of what Archetypal Improv is, and how it works.

Archetypal Improv is an approach I developed with a great deal of help from the other members of Amazing Spectacles in Cambridge (Gary Mooney, Denis Howlett, Netta Shamir, Justin ‘Rob’ Coleman, and several others). Using it, we were able to turn out some of the best long-form improv I’ve ever come across.

The style is based around the idea that in order to create really great improvised plays, players need to understand how stories are built and to have a way of communicating narrative ideas to each other. To provide a shared storytelling lexicon, Archetypal Improv draws inspiration from Joseph Campbell’s theories on the ‘Hero’s Journey’, along with observations from fiction, theater, and machine learning theory.

We use, broadly speaking, two ways to communicate story ideas--through plot, and through character. Communicating through plot helps drive the narrative forward. Communicating through character tells you where the plot should be going. For plot communication, Archetypal Improv focuses on the idea of trouble. For character communication, we focus on the role that each character plays with respect to the greater story--it’s archetype.

Many long-form improv approaches rely predominantly on plot offers and divide a play into sequential segments, often using a ‘story spine’. The archetypal approach instead attempts to build a kind of map of the story within the first few scenes that can drive many of the choices that follow. The map is created by making the purpose of each character clear.

The most basic play format used in Archetypal Improv is the Vanilla Six-Hander, or V6H. ‘Vanilla’, because the format is the basic flavor, and ‘six-hander’ because it makes use of six archetypes. The V6H is a training tool designed to encourage players to think clearly about character roles, and to give them almost all the information they need to complete a strong play within just three scenes. A quick outline of the V6H reveals the archetypes that we generally use.

Scene One: The Banishment
This scene introduces the following pair of archetypes:

The Protagonist or Hero
The protagonist is the focus of the play. It’s their job to desire change, to be flawed, and to have the capacity to learn. The role of the protagonist is telegraphed to other players by creating a character who’s empathetic and limited in their choices without being weak. The protagonist generally starts low status. Their interior monologue generally sounds like this: ‘Somehow, I’m going to make things better!’
As a rule of thumb, the best protagonist flaws are the ones you can imagine being reflected in the moral to a story. Eg: the flaw ‘has no self confidence, can lead you to create a play with the moral ‘always believe in yourself’. A flaw like ‘is terrified of chocolate’ isn’t going to take you very far. ‘Don’t be afraid of chocolate’ doesn’t lend itself to a compelling narrative arc.
Unlike in some other long-form methodologies, the protagonist doesn’t usually drive the narrative. Rather, the protagonist is affected by the offers of trouble that they receive from other characters. Having the protagonist make active choices that propel the play tends to create a ‘willing hero’, and leads the play to tend to take on a tragic format, which is fine, so long as you’re aiming for tragedy.
Using the first Star Wars movie as an example guideline, our protagonist would be Luke Skywalker.

The Banisher
The banisher’s job is to either pull or push the hero out of his or her ordinary world. Generally speaking, the role the banisher will have in the play depends on how that ejection happens. If the banisher, for instance, fires the protagonist from their job, then they’re not likely to feature strongly in the work that follows. We call this kind of character a ‘framing-device banisher’. If, instead, the banisher creates an ultimatum that drags the protagonist with them into a troubled world, they’re generally referred to as a ‘mentor’. Mentors often feature extensively in the play that follows and guide the protagonist on their path.
The banisher usually starts with higher status than the protagonist, because this makes it easier for the banisher to exert the necessary force to make the protagonist leave their world. It’s also much easier if the banisher has a pre-existing relationship with the hero. However, it’s not necessarily always the case that the protagonist is familiar with the banisher. Many interesting mentor banishers are characters who know who the hero is, but who are unknown to the hero.
The banisher’s self-talk is usually something like: ‘I must lead this person on to something better,’ where the person in question is the hero.
Our Star Wars example for this archetype is Obi-Wan Kenobi.

The players should aim to have the banishment happen by the end of scene one, but shouldn’t be worried if they don’t make it. There’ll be another opportunity in scene four. It’s worth pointing out, too, that a banishment doesn’t have to be physical. A change of emotional or physical state can effectively put a person in a new world even though they don’t go anywhere. Thus a banishment can be anything from being divorced, to being sent to China, to growing antlers.

With this scene, as with all others, it’s best to try not to have the offers in the scenes be too prescriptive about what follows. We want to know that the protagonist has been ejected into an uncertain world, but we don’t want to know too much about what that world is just yet. Over describing future events creates expectations that it can be very hard for other improvisers to follow through on.

A checklist for the banishment scene goes like this:

Establish an ‘ordinary world’ that the protagonist inhabits--preferably an imperfect but tolerable one.
Try to establish strengths and a flaw for the protagonist.
End the scene with the protagonist being forced to leave the ordinary world.

Scene Two: The New World
Scene two establishes the world that the protagonist is going to be banished into. Players should aim to set this part of the story in a context that makes things as awkward for the protagonist as possible, given their flaw and what else we know about them. The scene introduces two characters who have a shared problem that they can currently neither escape nor solve.
The archetypes we use here are:

The Catalyst
The catalyst’s job is to symbolize trust, and to be the character with whom the hero is going to build a strong empathetic bond that that will allow them to change. Catalysts are often rescued, or are rescuers somewhere in the play. In many stories, this character is what you might think of as the ‘love interest’, but their role doesn’t need to be romantic. The catalyst looks for clean, positive solutions to problems. They generally have lower status in their opening scene, and their self talk goes something like this: ‘there has to be someone out there who can save us’.
Our Star Wars example would be Princess Leia

The Turncoat
The turncoat represents an ambivalence of trust. This character isn’t necessarily evil, and isn’t driven by a clear agenda. Rather, their moral position is unclear. They act as a foil to the catalyst by being ready to consider all manner of dubious solutions to the shared problem. They generally have higher status, and their self talk is: ‘what’s in it for me?’
A turncoat can start on the side of good and go bad, or start as deeply unreliable and become a strong force for good by the end of the play. Both choices work, so long as this character creates opportunities for the hero to feel uncertain and betrayed.
The Star Wars example is Han Solo.

Here’s a checklist for scene two:

Establish a new world that’s the worst place you can think of that the protagonist might end up.
Establish a problem that the catalyst and turncoat are stuck trying to solve together.
Make the moral difference in attitude between the catalyst and turncoat clear.
End the scene with one of the characters going off to try to pursue some solution to the problem.

Scene Three: The Plan
In this scene, any confusion or unresolved offers from the previous two scenes should be folded into a clear direction for the story. The scene focuses on a ‘nemesis’ outlining his or her plan for the world to their ‘henchman’.

The Nemesis or Antagonist
The nemesis is usually what we think of as the villain. Their job is to create trouble that will propel the story. This character almost always has the highest status in the play. Their self talk says: ‘Nothing will stop me from achieving my goal!’
Our Star Wars example is Darth Vader
Whereas the role of the nemesis in the story often ends up as that of a villain, as an improviser, this role requires giving a great deal of attention and support to the other players. Because the nemesis has so much power, they generally create the trouble that the other characters need to overcome. This means that the nemesis has to provide a large number of helpful offers that both keep the story on track and give the others something to work against.
It’s also worth saying that the nemesis doesn’t have to be evil. Many interesting plays feature characters in this role who have a fixed agenda, but are also empathetic to the audience, at least in part. The more that the force driving the nemesis is recognizable to the audience, the more nuanced the character will feel.

The Henchman
The henchman helps the nemesis achieve their goal. They act as the nemesis’s eyes, ears, and hands, and often feature more frequently in the play than the nemesis himself. Also the henchman role can be comprised of a large number of characters, all of which are potentially disposable. Their status is lower than that of the nemesis, but often high with respect to the other characters in the play. Their self talk goes: ‘Through you, I have purpose and value,’ where the ‘you’ in question is the nemesis.
The henchman role has a large amount of comic potential, but this should be used cautiously, as a completely ridiculous henchman is less able to make plausible trouble for other players.
Our Star Wars example is every storm-trooper that appears.

The plan revealed in this scene should ideally clarify the offers made in previous scenes and make sure that the choice of which scene defined the ‘new world’ clear to all, just in case something untoward happened in scenes one and two. Ideally, the plan heaps trouble onto the ‘new world’ developed in scene two, but does so in a way that happens to make matters worse for the protagonist, even though usually the Nemesis character doesn’t usually know of the protagonist’s existence.

Here’s a checklist for scene three:

Establish a plan.
Bind together the ideas raised in scenes 1 and 2 into a coherent whole.
Establish the objective that will drive trouble in the play.
End the scene with the henchman being sent off to advance the plan.

That’s it. By the time the third scene is over, players have pretty much all the information they need to create a compelling story. If you don’t believe me, come along to a workshop and watch it in action. Do note, though, that it takes some time to develop the skill of making full use of everything that these scenes reveal. As a starting point in developing this skill, improvisers should ask themselves the following two questions when the third scene is over.

How does this story end?
What is the resolution for each signifiant character we’ve created in the course of this play?

What’s the lowest point I can imagine for this protagonist?
How bad can we make it for our hero before he learns to change? This is important, because this is exactly where the play needs to be headed before real change can be revealed.

With these tools in your hands, creating great plays is a hell of a lot easier. But before I finish, here are a few other important things worth mentioning.

First, the archetypes described here aren’t the same as those you’ll encounter in any book on the Hero’s Journey. That’s because they’ve been derived from the principles of character function, rather than from storytelling tradition. That means that unfortunately, further information is hard to come by on the web.
The mapping between standard archetypes and the ones used here is roughly as follows:

Protagonist>Hero
Banisher>Combines elements of Herald and Mentor, but isn’t strictly either
Catalyst>Not really covered in Campbell-style archetypes
Turncoat>Has themes in common with the Shapeshifter
Nemesis>Shadow
Henchman>Has themes in common with the Herald and Threshold Guardian

Secondly, it’s worth reiterating that the V6H is a training tool. Keeping the first three scenes of a play to the same structure each time will eventually start delivering predictable work. Once the flavors of each archetype have been well understood, plays can and should be started in any way that takes the cast’s imagination. Assuming that you know which character fulfills which role simply because of what scene they happened to appear in will result in confusion, and should be avoided, particularly as characters can shift from one role to another.

Lastly, the archetypes raised in the V6H are applicable to a huge range of kinds of storytelling, from written fiction to screenplays. However, crafting non-improvised stories to open like a V6H won’t make your story as strong as it could be. Compromises have been made in the structure of the V6H to help improvisers gain a clear picture of what’s going on in the minimum number of scenes.

Hopefully this is all clear. There’s tons more to say, of course, but I’m out of steam for now. If anyone has questions or comments, I’d love to hear them.

Multiple Launches

2010-03-10T08:55:00.000-08:00

Things here the Bay Area are starting to get interesting. Today I’ll have my second workshop with UC Berkeley grad students. Following that will be a series of seven more workshops for UC Berkeley open to grad students from all departments. I’m excited.

Meanwhile, on the other side of the bay, I’ve kicked off discussions with Olya Lapina of the excellent SFImprov group. Assuming that everything goes to plan, I’ll be running a two-year long-form improv development program with them that will result in a self-sustaining performance troupe. This should be a lot of fun. I’m thrilled at the idea of adding an archetypal improv team to San Francisco’s impressive pantheon of players.

Then, later in the spring, if things go well, I’ll be running two five-week communication skills workshop courses in Berkeley that’ll be open to the general public. This is all on top of some other splendid projects still in development. Notably, a possible one-day ‘basics of long form’ workshop with the marvelous San Francisco Comedy College. Looks like I'm going to be busy!

If you want to know more about any of these projects, just email me at *alex dot lamb at gmail dot com* and I will happily answer all questions.

What is Fun?

2010-03-02T09:34:00.000-08:00

What is fun? Fun is a rather nebulous phenomenon. People have wildly varying ideas of what activities are fun, yet we all know what fun feels like. Are things that are funny also fun by definition? If not, why do both fun and funny things tend to incur smiles and laughter? Specifically, why is improv fun, and what is the experience of fun actually for? This week, I’ve found myself trying to answer these questions. I think I’ve done it. And furthermore, the answer I’ve discovered is striking enough that it’s going to change how I train people from here on forward.

My adventure in search of the origin of fun started with the books on behavioral economics I’ve been reading recently. (More on that in later posts!) A lot of the ideas I came across reminded me of topics I’d encountered in my work on machine learning over the years. Out of curiosity, I found myself trying to build a kind of map to explain human emotions and social habits in terms of machine learning goals.

To provide a basic example, we can compare human pleasure and pain with the positive and negative feedback signals that you feed into a neural network to tell it whether it whether it got the answer to a question right or wrong. If we believe that this approximation is fair, we might then describe fear as what a learning system experiences when most of the candidate models of the future it can build at a given moment entail receiving negative feedback. In other words, the system has a high expectation of something painful happening to it. Excitement, by contrast, is what a learning system experiences when the most likely future models it can build feature positive feedback. No big surprises there, but I’ve gone on to try to play the same game a whole range of human emotions, including anxiety, achievement, guilt, pride, embarrassment and frustration.

Intriguingly, there are certain kinds of human experiences that are a lot harder to reshape in AI terms than others. Love is a good example. The best explanation of love I’ve encountered is the one in Stephen Pinker’s ‘How the Mind Works’, where he describes it in terms of Game Theory. Romantic love, he suggests, is an abandonment of rationality that advertises willingness to engage in a pair-bond. General-purpose AIs don’t have much of a reason to engage in pair-bonds, so we can’t expect much love from them. However parrots bond for life and we see behavior that looks a lot like real love coming from them, even though they’re not human.

The most puzzling such emotion, though, of all those I explored, was fun. Clearly fun is something deep and important, because you see something that looks very like it in dolphins, chimps, dogs and the young of almost any mammalian species you care to choose. Similarly, it’s hard to define because if you ask a sample of human beings what’s fun, you’ll get wildly varying answers. For instance, a significant fraction are likely to include ‘shopping’ on their list, whereas many others would rate the same activity as a kind of torture. Fun doesn’t obviously map onto any kind of planning function you find in machine learning, and it doesn’t appear to maintain or moderate any kind of social behavior. It doesn’t even appear to be easily describable in terms of Game Theory. So what’s going on? The answer, I suspect, lies in who craves fun the most, and how they get it.

Clearly, fun has something to do with play, and play has something to do with being young. Play, also, has something to do with simulating experiences. Otherwise, why would children bother playing ‘Families’ when there’s usually ample experience all around them? Fun is also a signaled emotion. It comes with big obvious indicators that can be read between members of social species in an instant. Apes smile and dogs wag their tails. By contrast, achievement, also a powerful sensation, comes with much more subtle cues. Pulling an ‘achievementy’ face is perhaps possible, but a lot harder.

What I would hypothesize, then, is that fun is a specific, dedicated emotion designed for learning. It’s associated with play, and play is the process through which that learning is acquired. (It doesn’t look like I’m the first person to have this idea. In fact, a little web research suggests that Dr. Stuart Brown has been onto this sort of thing for a while. I have his book ‘Play’ on order from Amazon.) Play learning happens through experience simulation, and the primary characteristics of play appear to be that the costs and gains to individuals are reduced to a level at which new behaviors can be explored without the players damaging their social standing by taking part. Fun is strongly signaled to inform others that experimental behavior is being tried out and that normal social costs shouldn’t be applied. Additionally, fun takes different forms for different people because the skills each person wants to practice are different, and likely to line up tightly with the attributes that person values about themselves.

The same model can be used to explain why something is funny. Various philosophers have tried to capture the essence of humor, and their theories can be roughly boiled down as follows:
* Superiority: We laugh at something because we feel safely above it. (Hobbes)
* Incongruity: We laugh at something because it breaks a pattern. We experience ‘frustrated expectation’. (Schopenhauer)
* Relief: We laugh at something to break the sense of tension over an experience. (Freud)

The ‘experimental zone’ theory of play would seem to encompass all these. We decide that an experience is funny when we decide that the net social cost of a statement or idea is negligible even though we might not understand it. We flag that we’re safety-boxing that experience by laughing at it. Applying this model to the theories above, we get the following:
* Superiority: When we feel safely distant from an experience, we signal it.
* Incongruity: When we experience an unexpected pattern break without cost, we signal it.
* Relief: We signal safety to indicate that a tense experience is being considered trivial or experimental, and therefore no longer dangerous.

Laughter has the added bonus of informing those around us that they’re part of a safe group who’re parsing an otherwise dangerous experience in the same way. Hence its infectiousness. Laughter becomes derisive when it’s clear that the boundary of who’s safe includes some individuals and not others.

This model adds up with many personal experiences I’ve had from improv training. For starters, people don’t really start to take on new behaviors until they feel safe. And that safety is defined by everyone in the group agreeing through laughter signals that the experiences they’re having shouldn’t be taken seriously. Also, certain games invariably work better than others, and those that work well seem to be the ones that reinforce that sense of a ‘safe space’ in which nobody is excluded.

The implications of this for teaching, I believe, are significant. Most importantly, I’d recommend not expecting people to try out and take on new behaviors unless they feel they can do so safely. This is why so many ‘role-play’ sessions feel cold and cringey. You know when people feel safe because they’ll signal it to you. Furthermore, rather than being an indicator of ‘mucking about’, laughter and play are the indicators that the brains of the players have activated their dedicated learning mode and are ready to incorporate new data.

From the perspective of soft-skills training specifically, the lesson go deeper still. It’s always tempting as a trainer to illustrate key points to participants by having them directly experience the effects of their own behavior, or the effects of the assumptions they make, through striking, unexpected demonstrations. The ‘experimental zone’ theory of fun suggests that we should only do this so long as it doesn’t impair the sense of safety that we’ve built. Otherwise, the learning potential of the experience is likely to be lost, and it may be remembered for all the wrong reasons.

In short, there seem to be solid reasons to believe that fun should be our compass in guiding us to develop training that produces real, persistent, and valuable change. If anyone has any counter-theories, though, I’d be delighted to hear them. This topic continues to fascinate me. From a relatively innocuous starting question, the results so far feel surprisingly profound.

On Respect

2010-02-25T15:20:00.000-08:00

This week I learned something very powerful and important about improv training from someone who’s done no improv training at all. Furthermore, I learned it from someone who’s been a part of my life for the last seven years and who I’d never really thought to have a deep conversation with on the topic. That person was my father-in-law.

My journey toward this moment started with a conversation I had with a terrific guy called Ari Hoffman. I’ve been learning from Ari about some groundbreaking work he was involved in to use improv training to teach his fellow medical students at UCSF. He had some profound things to say about the value of teamwork, and using the improv principle of ‘making your partner look good’ in a hospital setting. Most notably, he told me a story about a time when he was helping a woman give birth.

He explained that during childbirth, there’s more loss of control of certain bodily functions than many people expect, and that things can get messy. This means that the sheets or ‘chucks’ under a patient need to be changed from time to time--a task that’s generally considered to belong to the nurses. Ari explained how he’d channeled his improv training and changed the chucks to ‘save the scene’ as it were, while the nurses where unavailable. The nurses responded with unexpected delight, and felt like Ari had behaved much more like a part of the team than they were used to from medical students. Ari believed that it was that improv attitude that enabled him and his improv co-instructor Brynn Utley to graduate with honors from their highly competitive program.

This story really stood out for me, so when opportunity presented, I shared it with my father in law, Howard Graves, a doctor who until recently worked at SF General and is one of the most experienced Emergency Medicine specialists in the Bay Area. I was astonished at the vigor of this response.

“This is a really important point, Alex,” he told me, “and the way I teach it is this...”

He then went on to describe in enthusiastic detail a communication principle that seemed, at first glance, to be almost completely different from ‘make your partner look good’. What he told me was that in medicine, maintaining the dignity of patients is critical. Patients who walk into an emergency room are often at a huge emotional disadvantage. They frequently feel more vulnerable than they have in years. For the doctor handling that patient, another knock to that person’s confidence can mean the difference between their being able to give rational answers about their condition, and not giving useful answers at all. Retaining the dignity of your patients can be a matter of life or death.

When I went on to ask Howard about interacting with nurses or other doctors, he said the same principle applied. For a large number of historical reasons, modern medicine has inherited a rather hierarchical culture that sometimes can get in the way of the effectiveness of the people who comprise it. By taking personal responsibility for keeping up the dignity of everyone you interact with, they feel comfortable and confident working with you, and everything works better all round.

Of course, in truth, Howard’s message has more in common with ‘make your partner look good’ than at first I supposed. Both ideas encourage us to take responsibility for someone else’s experience and to make sure it goes well. The difference, I think, is that ‘make your partner look good’ is about achieving a shared goal. In improv, the good of the scene is placed above the experience of any individual player, and as a consequence, every player gains. In contrast, Howard’s idea often relates to situations where there may not be any shared goals or where goals may be in conflict. I think of this idea as ‘Second Person Status’.

The improv concept of Status is old and well-established. It essentially explains how every character in a scene broadcasts some level of intrinsic authority through their choice of posture, language, etc. Applied improv uses this concept to unpick the social dynamics of real-life situations. In applied improv terms, then, Second Person Status implies that when you find yourself in an environment plagued by a brittle emotional landscape, you can achieve a lot more by attempting to ensure that the status of those around you never drops. It’s a bit like learning to walk through a glassware showroom while wearing a backpack. You take each turn carefully.

I’ve been teaching people to read and pitch their status for years, and also how to flex when the situation requires it, but I feel like this idea is a little different. Taking personal responsibility for the status of others gets at the heart of what ‘respect’ in human society is all about. I suspect that this approach could be useful in academia, business, international relations, and any number of other arenas. I’m now looking forward to turning this idea into a sequence of targeted games and trying them out.

As my last word on the topic of respect then, I have this to say: Respect to Howard Graves and Ari Hoffman--two wise and excellent men.

Gift Giving

2010-02-14T14:55:00.000-08:00

This week I got to go back to Santa Cruz to run another session with my lovely UCSC grad student group. As before, this session was a kind of a grab bag of exercises I’d come up with--mostly as a reflection of the research I’ve been doing on influence. And once again, we encountered some fascinating results.

Perhaps the most striking game for me this time was a variant on an improv classic called Presents in which players take turns to give each other gifts. (You can find it in the improv encyclopedia.) I picked this game because I wanted to explore the impact of reciprocity in the clearest way possible, and this seemed like a natural place to start. (To understand what I mean by reciprocity, take a look at the previous post.)

Normally, a gift recipient in Presents gets to decide what gift he’s being given and little or no value is assigned to the emotion with which the gift is received. This time, instead, I had givers say what they were giving. I then asked recipients to be as honest as possible about how they felt about the gift they’d been given. The idea was that each player would gradually figure out how to make their partner happy, and the result of the game would be a sense of achievement and increased trust for each pair.

However, shortly after the game started it rapidly became clear that things weren’t going as planned. Some participants were much pickier about what they received than others. Also, people were having trouble tuning in to their partner’s preferences. With each transaction that passed, some faces showed looks of greater and greater discomfort. By the time I halted the game, many participants still hadn’t really figured out how to make their partner happy. The game had had the opposite effect to the one I’d intended.

Fortunately, the group I was playing with have developed terrific rapport and a huge amount of trust, so the outcome didn’t matter, but it certainly surprised me. In the discussion that followed, it became clear that several things had been going on. For starters, the discomfort started when people didn’t appreciate the very first gift they were given. On reflection, one can see that this is because the reaction to a gift is as much a part of the reciprocity process as the gift itself. People give a gift and hope to see delight. An unsuccessful initial transaction immediately weakened the relationship for that pair. This made it harder for players to be pleased by later offers.

Secondly, there wasn’t enough information in the game for people to tune in to their partner's likes. This was even though I had suggested before the game that players offer things that they themselves liked the first few times before trying to work out what their partner wanted. I think everyone started with so much confidence that they’d be able to meet their partner’s needs that they didn’t pay too much attention to revealing information about themselves.

Most significantly for me, though, was how much the game appeared to matter. I base this on the body language and facial cues that the game revealed. People *really* don’t like reciprocity transactions to go poorly. The habit goes very deep. So much so that we have created all sorts of social behaviors to conceal our true reactions to gifts. And, ironically, those that are closest to us are probably most likely to received a censored response.

The strength of this habit may in party explain the enduring ritual draw of Christmas gift giving, and why so many of the emotions that arise from it are nuanced and not entirely positive. The fact that reciprocity--a mechanism intended to increase mutual trust--is so powerful that it causes us to routinely conceal the truth from each other says something about the extraordinary subtlety of the relationships that human beings form.

On Hidden Mechanisms of Influence

2010-02-01T10:13:00.000-08:00

In the wake of the fun I had on my last visit back to Santa Cruz, I’ve been thinking more about Robert Cialdini’s impressive book on influence. (See previous post.) I’ve started to do with it what I always do with theories that interest me--to take it apart. (The engineer in me can’t resist.) An besides, I suspect that a weight of highly useful improv games lurk somewhere in this material, waiting to be teased out. Not to mention further valuable insights about how people interact.

Cialdini quotes six mechanisms through which human beings are influenced by others. These are:
Reciprocity: the urge to return favors.
Consistency: the urge to appear to have stable opinions and behaviors.
Social Proof: the habit of assuming that behaviors exhibited by others represent a standard for acceptability and wisdom.
Authority: the habit of assuming that those bearing the social trappings of power can be trusted.
Liking: the urge to make compromises when dealing with those with whom we have a pre-existing bond.
Scarcity: the urge to act quickly when we perceive a valuable commodity to be in limited supply.

Why six mechanisms? And why these six? Business book authors seem to be crazy about lists. (The five habits of great leaders. The seven guaranteed paths to selling. The twenty three easy steps to world domination, etc.) There is probably fascinating study to be done on this topic in its own right--to try to work out why lists hold such appeal. However, that’s not my focus here, and personally, I mistrust lists.

Whenever someone quotes a list at you, it’s effectively a statement that the system they’re describing has symmetry of that order. In other words, the structure that underpins the system can’t be broken down into some simpler pattern. In my experience, things in life seldom have symmetry as consistent, orderly and complex as order six without something going on underneath.

So how about these patterns of influence? Do they have a deeper structure? I think so. Right away, we can break Cialdini’s list down on the basis of the type behavior to which they relate. The mechanisms of Scarcity and Social Proof both relate to the behavior of individuals operating in anonymous groups. They’re not specific to people. You can see the same behaviors in shoals of fish, in their patterns of feeding and flocking. Feeding patterns reflect Scarcity: ‘food is suddenly available and I might not get some’. Flocking reflects Social Proof: ‘everyone’s turning left so there must be a good reason to do so’. The same primal patterns of social optimization can even be found in bacteria.

At the other end of the scale, the mechanism of Authority relates specifically to hierarchical behavior, which you only get in those organisms that collaborate in structured groups. Furthermore, it’s culturally dependent. A man dressed as a witch doctor with lion’s teeth around his neck is going to be less immediately authoritative to a western audience than a man in an Armani suit. This mechanism clearly equates to the improv principle of status.

Between these two extremes lie Reciprocity, Consistency and Liking, all of which relate to building and maintaining relationships with peers. Thus, we can see that there are three levels of social behavior being revealed: hierarchically-connected, peer-connected, and group-connected. (Intriguingly, these layers correspond to the first the scenes in a Vanilla 6-Hander improv play, but that’s perhaps material for another post.) The next question is whether we can slice up each these layers in ways that reveal further structure. Doing so might reveal gaps in Cialdini’s findings and uncover mechanisms that he missed. And this is where it gets interesting.

Let’s look at the group-connected layer first. What’s the difference between Scarcity and Social Proof? While they share the common idea that ‘other people know something I don’t’, I would suggest that Scarcity relates to group competition while Social Proof reflects collaboration of a very basic sort.

However, if we then look at the peer layer--Reciprocity, Consistency and Liking--a different pattern emerges. There’s no pattern in this set that obviously relates to collaboration versus competition. Instead, these three mechanisms appear to correspond to different points on a relationship-building time-line. Reciprocity relates to how people respond to overtures of alliance. Consistency encourages people to behave in a reliable fashion while a relationship is being developed so that others can build predictive models of their behavior. Liking reflects reluctance to break a social bond once it has been fully formed.

Then, last of all we come to the mechanism of Authority, sitting in a group all on its own. Can we infer the existence of other missing mechanisms to this group by applying the patterns we found in the other two groups? Perhaps. I haven’t managed this yet. However, there’s a more obvious omission here. Hierarchies of individuals generally don’t only involve information passing from the top to the bottom. Information percolates back up as well. Thus, given that human beings have been forming hierarchies for a very long time, shouldn’t we also see patterns of influence here too?

Of course we do. I would refer to this new principle as that of Vulnerability. Hard-wired examples involve the ‘looking up cluster’ that women apply to men. This involves body language that’s low-status, yet gently coercive. Consider also tropes such as the female hitch-hiker by the side of the road whose boyfriend suddenly appears when a ride is acquired. Many of us will also be familiar with how successful children can be at exerting social influence on their parents, even though their apparent bargaining position is very weak. I suspect that there are a host of influence tricks that belong in this bucket.

I think Cialdini missed this mechanism because it because it’s employed less frequently by sales-people and managers. However, this doesn’t mean that it’s not widely used. A female friend of mine won accolades in her younger days for her door-to-door fund-raising. She raised a staggeringly greater amount of money than her peers working in the same charity because her approach combined quick-witted dialog with a highly non-threatening appearance. Doors opened for her that would open for nobody else.

Thus, all told, we’ve now got seven mechanisms that occupy slots in a more clearly defined structure. What’s curious, though, is that each layer of social interaction we’ve defined comes with its own symmetry. This suggests that Robert Cialdini effectively nailed six out of seven of the major channels of human interaction--at least if you believe the model we’ve constructed here. That’s impressive. However, the lingering question in my mind still whether we can take what we’ve found here and use it to create behavioral games that enable people to do things they’ve never achieved before. And that’s something I’m still working on.

Influence and the power of social proof

2010-01-22T17:21:00.000-08:00

I recently read Robert Cialdini's 'Influence: The Psychology of Persuasion'. Not only is it a terrific book and a 'business classic', but I couldn't help also suspecting that it was dynamite material for improv workshops.

The workshops I ran at UCSC for the graduate students there had a very strong empowerment angle, so when the students decided to have a reunion, I got the opportunity to try to adapt some of the learning points in Influence to an improv context.

All the exercises were successful, but the most fascinating was the one I put together to teach the principle of 'social proof'. This principle outlines how human beings automatically take their cues about how to behave and what's acceptable from the behaviors of those around them.

In the classic improv game 'Yes Lets', everyone engages an a shared physical activity as they move through a room. Then, when someone has a suggestion for a new activity, for instance: flying like a bird, they call out 'let's all fly like a bird!' Everyone then calls out 'yes lets!' and then starts miming that activity.

I adapted this game by randomly handing out slips of paper to all participants beforehand. Every single piece had the word 'chicken' on it, except one, which said 'elephant'. Participants were instructed to look at their paper but not tell anyone what it said. Then I asked them to imitate the animal on their slip when I called out the magic words 'do your thing'.

I ran the game normally for a few rounds before calling out the change, and then watched what happened. Everone in the room started behaving like a chicken, but in slightly different ways. The set of acceptable chicken behaviors, however, rapidly stabilized. This made the person being the elephant in the room rapidly stand out. People started looking at her oddly. The game ended when the woman being the elephant buried her face in her hands and was caught between groaning and laughing so much that she couldn't continue.

Afterwards, people commented that they thought she'd just chosen to be creative in her way of being a chicken. They thought the arm being a trunk was supposed to be some sort of beak. They thought her trumpeting noises were supposed to be the crowing of a rooster. They assumed this even though there was no indication before the game started that I'd be giving most people the same animal to mime. Indeed, the sheer fact that I handed out folded, random slips might have tipped them off that I had something specific in mind. They had all automatically assumed that everyone was a chicken, just because most of them were.

It's worth pointing out that the participants here were graduate students--rational, highly educated, socially aware people who'd attended dozens of improv communication workshops already. Nevertheless, the power of social conformity proved almost overwhelming. I have to wonder what it says about the rest of us, and the way we parse the reality we inhabit each day. How many of the chickens we think we're seeing are actually elephants?

First Post

2009-12-15T16:37:00.000-08:00

I just moved to Berkeley. I had to leave behind my excellent improv troupe in Santa Cruz (Six Wheel Drive), and the wonderful grad students I taught there. Now I have to start again. From scratch. This time, I’ve decided to record the process and share it. It should be fun.