On the 20th of April, 2016, Robert Goldstone was elected to the American Academy of Arts and Sciences. He joins several others at Indiana University, including Richard Shiffrin, Linda Smith, and Robert Nosofsky from the Department of Psychological and Brain Sciences, and Douglas Hofstadter and Mike Dunn from the Cognitive Science Program, who are also members of AAAS. Read about it at: http://news.indiana.edu/releases/iu/2016/04/goldstone-american-academy-arts-sciences.shtml
Congratulations to the pair o’docs, Dr. Paulo Carvalho and Dr. Joshua de Leeuw, who commenced on the 6th of May, 2016. Speaking of paradox, If two graduate students organize all of the activities in a laboratory for those people, and only those people, who do not organize activities for themselves, then how will the laboratory continue to operate after they have departed?
Dr. Joshua de Leeuw will start in the Fall of 2016 as Assistant Professor in Cognitive Science at Vassar College. Meanwhile, Dr. Paulo Carvalho will start a position as postdoctoral research scientist at Carnegie-Mellon University, working with Dr. Ken Koedinger. Hearty congratulations to the both of them!
Without ever explicitly discussing it, groups often times establish norms. A family or committee might develop a norm about when it is acceptable or not for members to interrupt each other. People greeting each other in different countries have very different norms for whether to shake hands or kiss, and if to kiss, how many times and in what cheek order. In some countries, tipping is not the norm, but if it is, violating the tipping norm could make you a persona non grata at a restaurant. We (Hawkins & Goldstone, 2016) were interested in how social norms emerge in a group without its members explicitly deciding on them, and the factors that promote effective norms.
To help explore these questions, we started by considering a simple scenario we call “Battle of the Exes.” You and your romantic partner live in a small town and both love coffee. Your shared loved of coffee was not, alas, enough to keep you together, and you have now broken up. There are only two coffee shops in your town, one with much better coffee than the other. Both you and your ex want to go every day for coffee during your simultaneously occurring coffee breaks, but if you pick the same place and run into one another, neither of you will enjoy your break at all.
Neither you nor your ex want to sit down to negotiate a schedule, but can you nonetheless develop a satisfactory routine? One of you could always go to the better coffee shop, but that would not be fair. Each of you could choose randomly, but that would end up with you and your ex often seeing each other, which would not maximize your duo’s happiness, and would not provide a stable solution in the long run.
These three features — fairness, happiness maximization, and stability are generally useful ways to assess the quality of a group’s behavior. To study scenarios like “Battle of the Exes” in the laboratory, we developed an interactive, real-time, online game. On each of the 60 rounds of the game, two players are given the choice of moving their avatar to one of two circles — one that they can visibly see will give them a small monetary prize and one that will give them a large payoff. The only catch is that if both players move to the same circle, then neither player gets anything for that round. For half of the groups, there was a small discrepancy between the prizes (1 cent vs 2 cents), and for the other half, there was a large discrepancy (1 cent versus 4 cents). Also, for half of the groups, each of the players could see the other player’s moment-to-moment position as they moved to the circles (Dynamic movement), while for the other half of the groups, the players only see the final choice that the other player made (Ballistic movement).
568 players were matched together to create 284 two-player groups. Some groups developed behaviors that were fair and stable, and led to both players earning a lot of money. These groups tended to develop social norms even without explicit communication. For example, the players A and B would alternate over rounds who got the large payoff, first A then B then A…., leading to a pattern like ABABABABAB.
In terms of maximizing happiness, the dynamic condition led to better earnings for the players than the ballistic condition. When the players can see each others’ moment-to-moment inclinations, that helps them coordinate. The dynamic condition also led to fairer solutions than the ballistic condition, with players earning similar amounts of money. An implication of these results is that giving the members in a group more information about what each person in the group is currently thinking about doing can help the group achieve well-coordinated, fair and happy solutions. This is something for politicians, social network providers, and amusement parks to consider when they are trying to design social spaces for their groups. Mutual visibility of group members is often an effective way to promote coordination.
In terms of developing stable strategies, there was a striking interaction between payoffs and movement type. When there was not a large difference in payoffs, choices in the ballistic condition were more stable than in the dynamic condition. When the stakes were low, players in the dynamic condition simply relied on moment-to-moment visual information to figure out who should get the larger payoff on any given round. They did not feel a strong pressure to develop a norm because they could use their continuous information as a crutch to help them coordinate. However, when the stakes were high, with one circle earning four times what the other circle earned, then the dynamic condition developed significantly more stable solutions than the ballistic condition. For these particularly contentious, high stakes situations, it is useful for the players to develop strong norms to help them coordinate, and the moment-to-moment information about player positions helps to create these norms.
One clear measure of how much contention there is in a group is how long both players move toward the same high payoff option before one “peels off” and lets the other player have the high payoff prize. Using this objective measure, groups have more contention at the beginning of the experiment session than the end. The higher stakes condition has more contention early on than the lower stakes condition, but by the end of the experiment, that ordering is flipped. Groups that have more contention at the beginning of the experiment tend to have less contention by the end of experiment, and are more likely to develop clever strategies like alternating who gets the high payoff option from round to round. A take-home message from this result is that contention in groups is not something to be avoided. For the groups in our “Battle of the Exes” game, early contention gives rise to well-coordinated, fair, efficient, and happiness maximizing solutions by the end of the experiment. It may be tempting to try to pave over contention and disagreement in a group, but letting the group work through these contentions is often key to giving them the motivation and insight that they need to develop creative, well-coordinated norms like alternating who gets the better payoff over rounds. So, although it may have been contention that broke you and your ex up in the first place, there is hope that this kind of early contention may allow you to enjoy your superior cup of coffee in peace. At least on Mondays, Wednesdays, and Fridays.
Our “Creature League” study has been mentioned at Science Daily, ScienceNewsline, IU’s News Room, Medical Xpress, EurekAlert!, and Science Codex. Here’s an audio description of the work, courtesy of Academic Minute. Participants in the group behavior experiment of Wisdom, Song, and Goldstone (2013) tried to assemble teams of Pokemon-like creatures that scored well. Each creature was associated with a score for itself, but some pairs of creatures also produced positive or negative scores. Because of these interactions between creatures, the problem of assembling high-scoring teams posed a difficult search problem for participants. Participants could assemble their teams by 1) using their previous teams (status quo), 2) taking creatures from their historically best team (retrieval), 3) dragging untested creatures from the league of creatures (innovating), or 4) dragging individual creatures or entire teams from other participants’ solutions (imitating).
Some of the interesting results from this study were:
1) Participants tend to do BETTER when surrounded by imitators. One of the primary mechanisms for this is that when a person comes up with a good solution, their peers copy the solution, and sometime improve upon it. The person who was originally imitated can then benefit from these subsequent solutions (cliff swallows show a similar collective dynamic, with birds benefitting by being imitated while foraging). Imitation also acts as a cultural memory for what has worked well in the past. If an innovator’s solution to a problem is preserved by imitators, then the innovator does not have to remember their solution themselves.
2) As problem increased in difficulty, solutions were less diverse, and exploration was less prevalent.
3) Participants were more likely to imitate popular choices. above and beyond what would be expected from random copying of solution elements.
4) Participants are more likely to imitate a solution that is increasing in popularity among peers.
5) Participants are more likely to imitate solutions that are similar to their current solutions. This helps avoid hybrids/cross-breeds that don’t score well.
6) Participants begin a game by imitating and innovating relatively often, and end by more conservatively sticking to their existing solution. The best scoring strategy was to stick close to an existing solution, and innovating was worst.
7) At a group level, diversity of solutions decreased over rounds of a game. Bigger groups did better, but bigger groups also showed less diversity.
Here are some reports of our PLoS One paper on human collective behavior studying cyclic patterns in a generalization of the familiar rock-scissors-paper game. We find situations in which groups of people grow increasingly predictable as they cycle around a set of choice options in a game similar to rock-scissors-paper but with 24 rather than 3 choices.
‘Brain Calisthenics for Abstract Ideas’ article in the New York Times (June 6, 2011)
Searching in space and minds: IU research suggests underlying linkarticle in E Science News (September 12, 2008),UPI, Scientific American and Science Daily
Back when Dwight Eisenhower was president of Columbia University, he was asked how the university should arrange the sidewalks to best interconnect the campus buildings. He responded that they should first plant grass seed, let the grass grow, see where the grass became worn by people’s footsteps, and install the sidewalks in the most worn patches. The Percepts and Concepts Laboratory (directed by Chancellor’s professor Robert Goldstone, also director of the Cognitive Science Program) at Indiana University has put Eisenhower’s proposal to the empirical test, asking what kinds of trails people will spontaneously form when they are motivated to take advantage of the trails left by their predecessors. Early trail blazers through a jungle use machetes to make slow progress in building paths – progress that is capitalized on and extended by later trekkers, who may then widen the trail, then later put stones down, gravel, asphalt, and eventually an eight-lane highway.
In the article “Self-organized Trail Systems in Groups of Humans” (appearing in the July/August issue of the journal Complexity, available at http://cognitrn.psych.indiana.edu/papers.html), Robert Goldstone and Michael Roberts report the results of a group experiment in which people collectively travel among random destinations in a virtual world. As they step on a location, they change their environment, making it easier for subsequent walkers to step on the same location. In this way, a trail left by a walker often leads other walkers to follow the same trail, thereby reinforcing and extending the trail.
The trails that our experimental groups of participants created are compromises between people going directly to their destinations, and taking paths of least effort. The trail network that completely connects a set of destinations using the minimal amount of total trail length is called a Minimal Steiner Tree. While soap films reliably create Minimal Steiner Trees, our human collectives did not. However, their paths did deviate away from bee-line paths to destinations, in the direction of Minimal Steiner Trees.
We modeled our results by adapting a model from biophysics (Helbing, Keltsch, & Molnár, 1997) that is based on Brownian motion within a field potential, and has been applied to ant trails. This model, which assumes that travelers’ steps are a compromise between going where they want to go and where others have gone before, did a good job of reproducing the trails that our groups formed. The growth of our collectively produced trails offers the promise of revealing principles about how future progress is achieved by exploiting and extending prior innovations. Our experiments and simulations also provide a rigorous way of following the poet Antonio Machado’s exhortation: “Traveler, there is no path. Paths are made by walking.”
Goldstone, R. L., & Roberts, M. E. (2006). Self-organized trail systems in groups of humans. Complexity, 15, 43-50.
Helbing, D., Keltsch, J., & Molnár, P. (1997). Modeling the evolution of human trail systems. Nature, vol. 388, 47-50.
April 13, 2006
The Percepts and Concepts Laboratory (Directed by Chancellor’s Professor Robert Goldstone, also Director of the Indiana University Cognitive Science Program) applies formal computational and mathematical tools used to study complex systems in biology and physics to understanding human collective behavior. People participate in group-level patterns that they may not understand, or even perceive. Our goals are to conduct experiments that reveal the patterns that groups of people spontaneously create, and to develop computational models that show how these patterns emerge from simple interactions among people.
One common situation that we have formally explored is how groups of people distribute themselves to valuable resources. Morel hunters forage their environment for mushrooms, drivers patrol downtown for convenient parking spaces, web-users surf the internet for desired data, and businesses mine the land for valuable minerals. When an organism forages in an environment that consists, in part, of other organisms that are also foraging, then interesting complexities arise. The resources available to an organism are affected not just by the foraging behavior of the organism itself, but also by the simultaneous foraging behavior of all of the other organisms.
In a series of experiments, we have developed a novel experimental technique for studying human foraging behavior (Goldstone & Ashpole, 2004; Goldstone, Ashpole, & Roberts, 2005). We have created an experimental platform that allows many human participants to interact in real-time within a common virtual environment. Resource pools are created within this environment, participants vie for these resources, and we record the moment-by-moment exploitation of these resources by each participant. The participants’ task is to obtain as many resource tokens as possible during an experiment.
Groups of animals generally distribute themselves well to resource patches. For example, mallard ducks, cichlid fish, and dung beetles all approximately match their numbers to the amount of resource. If twice as much bread is thrown in one pond location than another, then about twice as many ducks will spontaneously go to the more plentiful location. Our groups of humans, recruited from psychology courses, are fairly efficient and about as smart, collectively speaking, as ducks, fish, and dung beetles. However, we also find two important collective inefficiencies in their harvesting.
First, we find that people do not distribute themselves in an extreme enough manner. For example, if one pool produces 80% of the tokens and the other pool produces 20%, people distribute themselves in about a 73%/27% fashion. People who harvest the richer resource patch tend to earn more tokens than those harvesting the poorer patch. If this proves general, our advice is for people to try harvesting the richer patch: fish in pond locations known to be plentiful, study for professions that are hot, and visit bars with attractive people. Even though rich patches will attract more competitors foraging for the same resources, the number of people will not keep up with the patch’s advantage if our experiments generalize.
Second, we find cycles in the harvesting rates over time. In our experiment, these cycles come in 50 second waves of migration into and out of patches. Due to random fluctuations, more people will end up at one patch than another. The people in this over-crowded patch will tend to become dissatisfied with their token earnings, and will decide to leave the patch for hopefully greener pastures elsewhere. However, if they cannot see other people’s movements, they do not realize that what has made them decide to leave is influencing others as well. The result is roughly synchronized waves of migration. Ironically, it is precisely because people share the desire to avoid crowds that migratory crowds emerge! When people can see where other people are in the virtual world, then these waves of crowding do not arise.
We have developed a computational model of foraging behavior that reproduces the results from our experiments (Roberts & Goldstone, 2005). In this model, we create simple rules for each of the agents in a population, and observe the collective patterns that emerge. The assumptions that are critical for getting human-like results are: 1) people are lazy (agents tend to go for tokens that are close), 2) people have inertia (agents tend to keep moving toward a selected token once they have started), 3) people go where the gold is (as the number of tokens in a patch increases, agents will congregate there), 4) people avoid crowds (when agents can see the other agents and all of the tokens in the virtual world, they tend to avoid crowds), and 5) people act like buzzards (when agents can see each other but not the tokens, then they use the presence of other agents to indicate that tokens might be nearby).
Web-citizens can experience these experiments for themselves by visiting http://groups.psych.indiana.edu/. This site offers several ongoing experiments that run continuously 24 hours per day. Participants are automatically grouped together into experiments and play in 4-minute rounds. If there aren’t enough human participants at any given time, then we generate artificially intelligent ‘bots’ to keep the humans company in the virtual worlds. As it turns out, these bots are exactly our computational models of how people forage for resources. Like the ‘human be-in’ events of the 1960s and modern flash mobs, people participating in these experiments can experience what it feels like to be part of a collective mind that adapts to its environment.
Goldstone, R. L., Ashpole, B. C., & Roberts, M. E., (2005). Knowledge of resources and competitors in human foraging. Psychonomic Bulletin & Review, 12, 81-87.
Goldstone, R. L., & Ashpole, B. C. (2004). Human foraging behavior in a virtual environment. Psychonomic Bulletin & Review, 11, 508-514.
Roberts, M. E., & Goldstone, R. L. (2005). Explaining resource undermatching with agent-based models. Proceedings of the Twenty-seventh Annual Conference of the Cognitive Science Society. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
‘Trailblazing’ Video Game Offers Model For Human Behavior’ IU Press Release (Sept, 2006) , also appearing inHouston Chronicle, TX – Sep 11, 2006, Myrtle Beach Sun News, SC – Sep 10, 2006, Macon Telegraph, GA – Sep 10, 2006, Belleville News-Democrat, IL – Sep 10, 2006, Contra Costa Times, CA – Sep 10, 2006, Biloxi Sun Herald, MS – Sep 10, 2006, Kentucky.com, KY – Sep 10, 2006, Duluth News Tribune, MN – Sep 10, 2006, Monterey County Herald, CA – Sep 10, 2006, Kansas City Star, MO – Sep 10, 2006, San Luis Obispo Tribune, CA – Sep 10, 2006, Charlotte Observer, NC – Sep 10, 2006
‘Believing Is Seeing’ article (June, 2004)