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DESIGNING COLLECTIVE BEHAVIOR IN A GROUP OF HUMANS USING A REAL-TIME POLLING SYSTEM AND INTERACTIVE EVOLUTION Daphna Buchsbaum1, Pablo Funes1, Julien Budynek1, Heiner Koppermann2 and Eric Bonabeau1 1

Icosystem Corporation, 10 Fawcett Street, Cambridge, MA 02138, USA {daphna, pablo, julien, eric}@icosystem.com 2 ChangeWorks GmbH & Co. KG, Untere Albrechtstr. 17, 65185 Wiesbaden, Germany [email protected] ABSTRACT example of a human game that can be played in small groups [8] [9], we have shown that this approach is particularly powerful as an exploratory design technique, when the aggregate-level capabilities of the system are not known.

Interactive evolutionary design, a powerful technique where one marries the exploratory capabilities of evolutionary computation with the aesthetic skills and domain knowledge of the human as selective agent, has been demonstrated to be an extremely powerful exploratory design method. One of interactive evolutions most promising uses is in discovering individual-level rules of behavior and interaction that will produce a desired collective pattern in a group of human or non-human agents. The problem of finding micro-rules that produce interesting macro-behavior poses significant challenges, all the more so when what constitutes “interesting” macro-behavior may not be known ahead of time. Here, the system at our disposal is a real-time crowd polling and display system, whose potential for generating interesting group behavior remains largely untapped. Additionally, because the system is capable of polling large crowds of people in real time, it presents an ideal framework within which to take advantage of a relatively unexplored form of interactive evolution – collective evolution, where the opinions of the entire group are taken into account in the design of the next generation. Collective evolution has a broad range of potential applications, including marketing research and logo and brand name design.

Designing the individual-level rules of behavior and interaction that will produce a desired collective pattern in a group of human or non-human agents is difficult because the group’s aggregate-level behavior may not be easy to predict or infer from the individual rules. For example, the aggregate-level properties of traffic jams [10], a crowd evacuating a public space [11] or the stock market [12] cannot easily be derived from knowing the rules of behavior and interaction of drivers, people or investors. Agent-based modeling (ABM) [8] [13] [14] [15] or microsimulation, is often the only way to capture the emergent properties resulting from the behavior and interactions of the group’s constituent units or agents. While ABM is useful in producing aggregate-level patterns from individual-level rules, finding the appropriate rules still requires manual search and tinkering when (1) the collective-level patterns may be difficult to formalize into a mathematical detector and therefore the evaluation of a solution cannot be automated, and/or (2) the collectivelevel patterns made possible by the individual-level rules are not even known ahead of time.

1 INTRODUCTION Interactive evolutionary computation is an approach that combines the power of computational search with the skill of human evaluation [1]. Originally developed to generate interesting images and pieces of art [2] [3] [4] [5], the technique has become a powerful exploratory design method.

In this paper, we apply interactive evolution to the design of collective behavior, using an interactive polling system described by Kelly [16]. As an example of swarm behavior, in Kelly’s poetic description, a group of people use the polling system to collectively operate a simulated airplane.

One key application of this approach is in designing individual-level rules of behavior and interaction, in order to produce interesting collective-level patterns. The technique (see [1] for a review) is a directed search evolutionary algorithm which requires human input to evaluate the fitness of a collective-level pattern (here, the fitness might be how close the collective-level pattern is to a desired pattern, or how appealing the pattern is), and uses common evolutionary operators such as mutation and crossover [6] [7] to breed the individual-level rules that produced the fittest collective-level patterns. Using a simple

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Interactive evolution is not only useful as an approach for designing collective level behavior, it can also be used as a method for empowering a group to participate in a collective design process. In this case, rather than having a single human provide the fitness function, the group as a whole does so. There are numerous relevant applications for collective design, many of them in the area of product marketing, an area where large “focus” groups are already frequently polled for their preferences and reactions. By using collective evolution, the group can quickly and

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dynamically create logos, brand names and other artifacts that are maximally satisfying to the group as a whole, in a process that is entertaining for the participants. In this paper, we describe a number of collective design applications using the polling system.

basic functionality is the ability to poll a large group. Figure 2 shows two examples of polls that can be taken. Figure 2a shows the most basic poll question, requiring a yes/no answer. Multiple-choice polls are possible but require more time. Figure 2b shows a binary poll with two groups

2 REAL-TIME POLLING SYSTEM The real-time polling system, developed by Pixar’s Loren Carpenter [16] and licensed exclusively to ChangeWorks, uses a unique computer-based optical system that allows real-time and simultaneous interaction with large groups of people. Each participant holds a red/green reflecting wand (Figure 1a). A system of high-resolution cameras scans the entire room and detects which color each participant is holding up. This signal is then used by a computer and thus each participant’s response can be sampled and used for further signal processing.

answering the question. The audience is split into two teams and their answers compared. It is possible to split the audience into up to eight teams if required. a

b

Figure 2 a) A simple poll showing the audience’s responses to the speaker. b) A poll where the audience was split into two groups.

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b

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Figure 3 a) A game of collective pong. Each team controls a fence. Turning their wands to the green side moves the fence up, turning them to the red side moves the fence down. b) Collective flight simulator. One group controls left-right movement while the other controls up-down. As a whole they steer towards graphical targets.

Figure 1 a) The reflecting wand. One side is covered with red reflective tape, and the other side is covered with green reflective tape. b) Feedback screen displaying the seating chart. The seating chart accurately displays each participant’s responses in real time.

Using the system, various applications can be realized among which are: polling methods, team tasks and games such as collective pong or flight simulation (described later), or quiz show formats. This system has the advantages of requiring little installation effort, generating results and responses in real-time, allowing manifold polling methods. Swarm intelligence can now be a live and lively experience.

Other, more sophisticated functions can be designed, such as the two games shown in Figure 3 . Figure 3a shows an example of a collective game of pong. In this competitive game, the two teams each control a fence that can move up and down along the outer side of the screen. Behind both fences are animated cats. Between the fences, a dog rushes back and forth. The objective is to bounce the dog back towards the opposing team by moving your fence up and down and thus "protecting" the cats on one's side. Showing green moves the fence up and red down. Even teams of several hundred people can play against each other, leading to an impressive demonstration of how group intelligence emerges. Figure 3b shows the flight simulator described in Kelly’s book [16]. This co-operative flight task is designed to give the group an experience of collective success. It challenges the group to control an animated jet plane, guiding it through several targets on a mountain

One of the basic functionalities of the system is giving the participants feedback about what the rest of the participants are doing. For example, a feedback screen shows who is green and red in the room (Figure 1b ). The seating chart displays each participant’s individual signal choice and thus also acts as convincer that indeed everyone in the room is taken into account. This application demonstrates the unique real-time capabilities of the system by having, for example, several teams show red or green and immediately seeing the result on the screen. Another

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range. The left hand side of the room controls the rudder and the right hand side the flaps. After a short learning and practice phase, the group becomes proficient at flying the plane.

Depending on the starting condition, this simple set of rules can lead to an infinite number of patterns, including static patterns, oscillatory patterns and decaying patterns. An example is shown in Figure 4.

3 POTENTIAL APPLICATIONS AND INTERACTIVE EVOLUTION Now that we have introduced the polling system and its basic capabilities, it’s time to look at some of its possible applications, and the ways in which the system could leverage – and be leveraged by – interactive evolution.

Figure 4. A very simple example of the Game of Life (after Gardner [19]). The starting configuration is shown in the leftmost panel. Each cell follows the rules described in section 3.1. The game passes through the illustrated stages before stabilizing in the pattern shown in the right-most panel.

One natural setting for this type of polling system is at large events, especially those occurring in a stadium setting (e.g. sports events, concerts) where patrons are already used to seeing advertisements, graphics and even themselves on a large screen, and are inclined to participate in group activities. As a simple example, it is already common for the audience at a sporting event to do “the wave”, which can be seen as an emergent collective behavior. In order to perform the wave, each audience member follows a single simple micro-rule: stand up and wave my hands right after the person on my left (or right) has done so. This very simple rule produces an interesting emergent pattern; we can imagine additional appealing patterns of behavior emerging when the participants follow other rules, or even a combination of rules. If each audience member has a polling wand, and a view of the overall stadium seatingchart (as in Figure 1b) the question of what emergent patterns and behaviors can be generated, using which rules, becomes even more intriguing.

3.1

Conway’s Game of Life is an example of a game that can easily be played using the polling system presented here, substituting red and green for black and white. Feedback from the seating chart makes it relatively easy for people to determine the state of their neighbors. This is also an appealing game for a large audience to play, because it is capable of generating a large variety of patterns and behaviors from a universal rule set (i.e. everyone in the audience is following identical rules), eliminating the need for extensive planning and coordination of instructions and seating arrangements ahead of time, and allowing the group to witness the emergence of complex behavior from simple rules first hand. Additionally, a fundamental characteristic of the rules chosen by Conway is that it is extremely difficult to predict from initial conditions what patterns will emerge, and whether they will become stable, oscillate or fade away [19]; Similarly, it is not easy to determine which patterns could possibly be formed by following these rules. Therefore, the Game of Life fits our two criteria from the introduction: • The collective-level patterns may be difficult to formalize into a mathematical detector and therefore the evaluation of a solution cannot be automated • The collective-level patterns made possible by the individual-level rules are not even known ahead of time. As a result, interactive evolutionary design may be the best approach to discovering initial starting configurations that will allow the audience to collectively form interesting patterns and behaviors by playing the Game of Life.

INTERACTIVE EVOLUTION OF COLLECTIVE BEHAVIOR

A critical feature of this real time polling system is that it essentially turns the stadium (or other venue) into an extension of a cellular automation [17] [18]. Traditionally, a cellular automation is a collection of “colored” cells (in the simplest case, black or white), placed in a grid. Over a series of discrete time-steps the cells change color, following rules based on the color state of neighboring cells. The most famous example of a cellular automata is Conway’s Game of Life [19], which operates according to the following simple rules: • Each cell looks at the surrounding eight cells (above, below, left, right and diagonals) • If fewer than two of these cells are black and the cell is black it “dies” of loneliness (turns white) • If greater than three of these cells are black, and the cell is black, it “dies” of overcrowding (turns white) • If the cell is white, and exactly three of the surrounding cells are white, it is “born” (turns black)

3.1.1

EVOLVING EMERGENT PATTERNS

In order to evolve interesting patterns (such as those generated by the Game of Life) to be played by an audience in a stadium or other large gathering, we can use a variant of a standard interactive evolution interface, which has

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previously proven effective at designing interesting collective behaviors [9]. The basic interface presents multiple “playgrounds” (the standard is six) each of which contains an ABM. In this case, each playground would be made up of a grid representing the seating arrangement of the desired venue, and agents representing the human participants.

rule such as: “if your neighbors are doing this, then do that”. In simulating human behavior, two key components need to be understood: 1. Error. A probability of error is deduced, and calibrated, representing the actual distribution of error observed in humans. Some agents are set to be more error prone than others, for example, and so evolved behaviors need to be robust to this type of uncertainty (noise).

Genetic Encoding In order to find collective emergent behaviors using an Interactive Evolution interface, a genetic encoding is used to represent and mutate a suitable universe of agent/human behavior [9].

2. Synchronization. A simpler kind of simulation assumes the existence of an external synchronization signal, such as a one given to the participants via the main display board, that is visible for all. Even more interesting behavior is possible, however, when instead a delayed reaction is assumed. A stadium of people doing “the wave” is possible, for example, because differences in reaction time average out, creating a robust behavior.

Each genotype is composed of one chromosome per human participant, encoding the initial conditions, and/or rules for that agent to follow. In the simplest case, mutations affect the initial condition only (i.e. whether the person starts out showing red, green or nothing), and the rules of the game (e.g. Life or “the wave”) are fixed. This case already affords many interesting emergent phenomena, as described in the previous section. A much larger universe of prospective patterns is afforded when the rules themselves are allowed to evolve. The “game rule” genes, in number of three, each encode a trigger rule as follows:

3.2

COLLECTIVE EVOLUTIONARY DESIGN

Another intriguing possibility that is enabled by the real time polling system is collective design. Mankind has come up with methods, such as voting for example, for collective decision-making. Such methods are inherently limited, however, to making a choice or ranking among a preexisting set of options. Collective interactive evolution is a new form of group organization that affords the possibility of open ended collective design. This is very different from a multiple choice option, because the mass is actually generating the array of options and creating an increasingly satisfactory solution that is the emergent result of a collective form of intelligence. Ideally, the end product represents the gestalt of the collective opinion (see Funes and Pollack [20] and Blackwell [21] for some examples of collective evolutionary design).

1. Number of surrounding neighbors involved (0, through 8) 2. Modifier, which can be one of “.”, “-” or “+”, meaning “exactly”, “or more”, or “or less”. 3. Neighbor color: Green or Red. 4. Response color: Green, Red or Flip. Combining the four units above one can make rules like those in the game of Life, for example: 3.GG translates to “if exactly three neighbors are green, show green”; and 4+GR translates to “if four or more neighbors are green, show red”.

As mentioned previously, collective design can be a particularly powerful approach to creating memorable and representative logos and icons. For example, at a recent conference, a prototype collective design system was used to evolve emoticons that the audience felt best represented two emotions: Happiness and Trust (based on the facial expression evolution in [22]). An interactive evolution interface similar to that described in the previous section was used, and could be accessed individually by participants as an applet (shown in Figure 5).

In general, the rules for the Game of Life are too complicated for human agents to follow, leading to high levels of error. In practice, it may be useful to restrict the allowed genes to encode only for simpler rules, such as “if neighbor on the left is green, show red”, and so on. Simulating Human Behavior It is understood that humans cannot embody cellular automatons per se, because humans act as true independent agents. This is why human rules are modeled, that reproduce the ways in which people are likely to interpret a

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are instead all viewing the same choices simultaneously on the main display. It must be very clear how to vote for a given face, and what the collective results of voting are. Second, we are particularly interested in the case of marketing research being done in the context of a sporting event, concert, or other large gathering. In this situation, it is critical for the system to be extremely intuitive, so as not to need much tedious explanation, and very fast paced, so as to maintain attention and achieve noticeable results during what is likely to be a short break between other activities. To these ends, we have come up with an extremely simple, fast paced version of the emoticon evolution, to be used with the real time polling system. In this set-up, only three faces at a time are displayed: The current “champion” and two mutants. Audience members can vote for the left-hand mutant by showing green, and the right-hand mutant by showing red, or they can abstain, indicating that they prefer the reigning champion. During the voting period, colored bars next to each face indicate in real time how many votes each face is currently receiving. After a set period of time has elapsed, the votes are tallied. If either of the mutants gets a majority of the votes, it is named the new champion, and placed in the middle of the screen. Two new mutants are then generated based on the new champion’s genotype, including some cross-over with the previous champion and the losing mutant, based on the number of votes they received. If neither of the mutants received a sufficient number of votes, then the current champion continues his reign, and two new mutants are generated.

Figure 5. Interactive Evolution interface for collectively evolving a Happiness emoticon.

Initially, users selected the faces they felt best represented the given emotion, and these individuals were used to generate the next generation of faces (each face has genes controlling the position and size of various facial features). However, in addition to variants of the faces the user had selected themselves, the next generation also contained faces that represented the current most popular choices among all the users (i.e. those faces that had been selected by the highest number of users). Additionally, the fitness of each face was determined by the number of people who had selected it. The resulting faces with the highest fitness are shown in Figure 6.

a. Happiness

b. Trust Figure 7. Example Collective Evolutionary Design interface for use with the real time polling system. Middle face is the current “champion” and the two side faces are mutants of the champion. Participants can vote for the left-hand mutant by showing green, and the right-hand mutant by showing red. Abstaining is equivalent to voting for the current champion. Bars beneath faces represent the number of votes they’re currently receiving.

Figure 6 Results of a preliminary run at collectively evolving emoticons for a) Happiness and b) Trust

3.2.1

ADAPTING COLLECTIVE DESIGN FOR THE POLLING SYSTEM

In order to function with the real time polling system in a variety of large crowd situations, a number of adaptations must be made to the collective evolutionary design interface described above. First, all the participant’s no longer have their own Interactive Evolution interface, but

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3.2.2

COMBINING COLLECTIVE BEHAVIOR AND COLLECTIVE DESIGN

and design anything from brand logos to company names to product packaging (see Figure 9).

While the collective design interface described in the previous section allows for a fast-paced fun design process, it does not require swarm behavior per se. While the decision is made collectively, each participant acts as an individual. An interesting alternative is a collective design process in which the group must coordinate their behavior in order to make a choice. We can imagine just such a collective evolutionary design interface, which would function much as the game of collective pong described in section 2 did (example shown in Figure 8). Here, the choices are presented in a vertical column, and the group must turn their paddles to green or red in order to move a selecting cursor over to their top choice.

Lastly, although the application sketched here is simple, this approach to designing self-organizing systems has a wide range of applications, from collective robotics to distributed control. One example: radio-frequency tags, known as RFIDs. Although RFIDs have recently become very popular, most users intend to use them with a centralized mindset without knowing what a self-organizing collection of RFIDs might be able to do collectively. Our exploratory design approach enables an open-minded search for the hidden capabilities of such a system.

Figure 9. An interface for interactive evolution of words and phrases. Similar interfaces could be used to evolve brand names and catch phrases. Figure 8. An interface for combining emergent collective behavior and collective evolutionary design. The group must turn their paddles to red or green in order to select an emoticon for evolution.

4 REFERENCES [1] Takagi, H. 2001. Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89: 1275-1296.

This interface would present an interesting challenge to the group, since settling on a desired face is more complicated than simply turning your paddle to the color that moves towards that face (e.g. if the cursor is below the desired face, everyone turning their paddles to green could cause an overshoot). Similarly, it would be interesting to see how the group deals with opposing view points – if half the room wants the top face (green) and half wants the bottom (red), splitting along those lines will settle on an undesired choice in the middle of the column, requiring a different strategy to come to a satisfactory result.

3.3

[2] Dawkins. R. 1987. The Blind Watchmaker. W. W. Norton, New York. [3] Sims, K. 1991. Artificial evolution for computer graphics. Computer Graphics 25: 319-328. [4] Sims, K. 1992. Interactive evolution of dynamical systems. Pages 171-178 in: Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life (F. J. Varela & P. Bourgine, eds.), MIT Press, Cambridge, MA.

CONCLUSION

Although the evolution of emoticons is a relatively simple (and silly) example, this process has also been applied to other areas of graphic design, and could be used to evaluate

[5] Sims, K. 1993. Interactive evolution of equations for procedural models. Vis. Comput. 9: 446-476.

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[22] Bulhak, A. 1995. Evolution of Facial Expressions. Honors Thesis, Monash University.

[6] Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing. [7] Forrest, S. 1993. Genetic algorithms: Principles of adaptation applied to computation. Science 261: 872-878. [8] Bonabeau, E. 2002. Agent-based modeling: methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. USA 99: 7280-7287 [9] Funes, P., Orme, B. & Bonabeau, E. 2003. Evolving emergent group behaviors for simple humans agents. Pages 76–89 in: Proceedings of the 7th European Conference on Arti.cial Life (ECAL 2003) (P. Dittrich, J.T. Kim, eds.), Dortmund, 14–17 September, 2003. [10] Helbing, D., Farkas, I. & Vicsek, T. 2000. Simulating dynamical features of escape panic. Nature 407: 487-490. [11] Still, K. G. 1993. New computer system can predict human behaviour response to building fires. Fire 84: 40-41. [12] Palmer, R. G., Arthur, W. B., Holland, J. H., Le Baron, B. & Tayler, P. 1994. Artificial economic life: a simple model of a stockmarket, Physica D 75: 264-274. [13] Reynolds, C. 1987. Flocks, herds, and schools: a distributed behavioral model. Computer Graphics 21: 25-34. [14] Epstein J. M., Axtell R. L. 1996. Growing artificial societies: social science from the bottom up. MIT Press, Cambridge, MA. [15] Aexelrod R. 1997. The Complexity of Cooperation. Princeton University Press, Princeton, NJ. [16] Kelly, K. 1994. Out of Control. Addison-Wesley. [17] Von Neumann J. et Burks A. ed., Theory of SelfReproduction Automata, University of Illinois Press, 1966 [18] Wolfram, S. 2001. A New Kind of Science. Wolfram Media.

[19] Gardner, M. 1970. The fantastic combinations of John Conway's new solitaire game "Life". Sci. Am. 223, 120-123. [20] Funes, Pablo and Pollack, Jordan B. (2000). Measuring Progress in Coevolutionary Competition. From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior. Meyer, J. et. al (eds.) MIT Press. Pp. 450-459. [21] T. Blackwell, Swarm music: Improvised music with multiswarms, in Proc AISB'03 Symposium on artificial intelligence and creativity in arts and science, pp. 41-49, 2003

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