agent-based model

Towards Realistic Models for Evolution of Cooperation

One Sentence Summary:
The five major approaches to answering how cooperation emerges and becomes stable in nature (Group Selection, Kinship Theory, Direct Reciprocity, Indirect Reciprocity, and Social Learning) might be improved by not presuming asexual and non-overlapping generations, simultaneous-play for every interaction, dyadic interactions, mostly predetermined and mistake-free behavior, discrete actions (cooperate or defect), and the trivial role of social structure and social learning of individuals.
Disciplines:
Biology
Cultural Evolution
Sociology
Findings:
  • Observer-based reciprocity relaxes the requirement that each individual's likelihood of cooperating be known globally by introducing randomly selected observers. Even though interactions are only visible to these observers cooperation can still evolve showing "that cooperation may evolve through indirect reciprocity with or without global knowledge about agents' image scores."
  • Darwin's notion of the "survival of the fittest" does not specify what "fittest" refers to, and for good reason: the outcome of a behavior in each contingent situation determines its fitness. Different interpretations of "fittest" lead to different models for how natural selection works and therefore offer different explanations for the evolution of cooperation.
Keywords:
trust
reputation
reciprocity
evolution
cultural evolution
cooperation
competition
bioeconomy
altruism
agent-based model
Author(s) / Editor(s):
Published in:
MIT LCS Memorandum
Date:
2002
One Paragraph Summary:

Sociological and biological observations of humans and animals show that cooperation is an inherent part of human life and the life of many animals. This poses two questions: how do cooperative strategies become stable within evolution? And, how does cooperation emerge initially? Even though researchers have tried to answer these questions for at least a century, existing models do not fully explain why cooperation evolves. There are five major approaches: Group Selection, Kinship Theory, Direct Reciprocity, Indirect Reciprocity, and Social Learning. Each of these models explain only a few aspects of cooperation and might be improved by dropping some unrealistic assumptions: asexual and non-overlapping generations, simultaneous-play for every interaction, dyadic interactions, mostly predetermined and mistake-free behavior, discrete actions (cooperate or defect), and the trivial role of social structure and social learning of individuals.

The Evolution of Strategies in the Iterated Prisoner's Dilemma

One Sentence Summary:
The genetic algorithm uses computer simulations to evolve different strategies for playing Prisoner's Dilemma games, and by observing the interactions of populations of agents over many runs, it is possible to make useful observations that could generalize to human behavior – such as the tendency of reciprocation to establish itself and spread if cooperating agents are able to encounter one another.
Disciplines:
Biology
Computer Science
Economics
Political Science
Information
Findings:
  • Genetic algorithms, developed for complexity and artificial life research, can be used to evolve strategies for playing Prisoner's Dilemma games that are well-adapted to different environments, and thus can be a probe of possible dynamics of human cooperation.
  • From a random start, populations of Prisoner's Dilemma strategies evolve away from cooperation to less cooperative rules, but after a number of runs, those players that reciprocate when encountering cooperation lock into mutually beneficial reciprocal cooperation: reciprocity, once established, can spread through a population that is originally dominated by non-cooperative strategies.
  • Genetic algorithms are highly effective method of searching for successful strategies in very large possibility spaces.
Keywords:
agent-based model
complexity
evolution
game theory
prisoners dilemma
reciprocity
tit-for-tat
Author(s) / Editor(s):
Date:
1987
One Paragraph Summary:

John Holland at University of Michigan developed a means of testing computer problem-solving methods by applying a method based on Darwinian evolution: agents (program) have a phenotype (the strategy the program uses for problem solving) and a genotype (the way strategies are represented in their programming code). Means of reproduction and mutation are specified. Agents interact with each other in a rigorously specified simulation, and the effectiveness of each agent is evaluated in a particular environment in relation to its interactions with other agents; successful strategies are reproduced at a higher rate than less successful strategies; pairs of successful offspring strategies are mated by combining genetic material; mutation is introduced. Simulations can be halted after specified numbers of runs and analyzed, then restarted. In about a quarter of simulation runs with sexual reproduction, better strategies than Tit-for-Tat evolved, and after a random start, populations tend to first evolve away from cooperation as less cooperative rules succeed more often, but can evolve back toward stable cooperation states if cooperative strategies encounter one another and reciprocate.

The Evolution of Cooperation

One Sentence Summary:
"The objective of this enterprise is to develop a theory of cooperation that can be used to discover what is necessary for cooperation to emerge."
Disciplines:
Political Science
Sociology
Findings:
  • The emergence of cooperation can be seen as a consequence of agents pursuing their own interests. It is not necessary to assume that those agents are more honest, more generous, or more cooperative per se.
  • What makes it possible for cooperation to emerge is the fact that the agents might interact again. The choice made now of whether or not to cooperate will affect choices made in later interactions. This called the 'shadow of the future.' The shadow of the future can exist even when the participants are unaware of it, as is the case in biological cooperation (symbiosis).
  • No best rule exists independently of the strategy being used by others. Despite this fact, robust strategies, useful in many contexts, are possible.
  • The evolution of cooperation requires high levels of reciprocal interactions between agents. The absolute number of agents can be small as long as their interactions are numerous.
  • Communities of cooperation, once established, can protect themselves from 'invasion' by less cooperative strategies. "The gear wheels of social evolution have a ratchet."
  • The winning tit-for-tat strategy:
    1. Don't be envious. Don't compare your success to others, only to your own strategic possibilities, i.e. are you employing the best strategy you have?
    2. Don't be the first to defect. Cooperate as long as others are cooperating.
    3. Reciprocate both cooperation and defection. Enforcing the rules is as important as playing by them.
    4. Be transparent. In order for others to coordinate their choices with yours, they have to understand your behavior. Keep it simple and out in the open.
  • Ways to promote cooperation:
    1. Enlarge the shadow of the future. Increase the permanence of cooperative choices or the frequency of interactions.
    2. Change the payoffs. Make the long-term incentives to cooperate greater than the short-term incentives to defect.
    3. Socialize reciprocal cooperation as a norm. Teach people to cooperate first.
    4. Improve collective memory. Collective memory, or culture, is embedded in institutions. Provide access to collective memory.
  • The foundation of cooperation is the durability of the relationship, which allows agents to learn about each other in order to cooperate.
Keywords:
assurance game
agent-based model
communication
cooperation
norms
prisoners dilemma
reciprocity
reputation
security
tit-for-tat
trust
Author(s) / Editor(s):
Published in:
Basic Books
Date:
August 1, 1985
One Paragraph Summary:

Why do people (or other actors) cooperate? "The objective of this enterprise is to develop a theory of cooperation that can be used to discover what is necessary for cooperation to emerge." It uses the Prisoner's Dilemma as a framework for testing theories about balancing self-interest and competition.

One Page Summary:

Chapter 1, The Problem of Cooperation. Why do people (or other actors) cooperate? "The objective of this enterprise is to develop a theory of cooperation that can be used to discover what is necessary for cooperation to emerge." It uses the Prisoner's Dilemma as a framework for testing theories about balancing self-interest and competition.

"In the Prisoners' Dilemma, the strategy that works best depends directly on what strategy the other player is using and, in particular, on whether this strategy leaves room for the development of mutual cooperation."

Chapter 2, TIT FOR TAT. "The iterated Prisoners' Dilemma has become the E. Coli of social psychology," yet people have not paid much attention to how to play the game well. Axelrod organized a computer tournament to which people familiar with PD submitted programs encoding different strategies. The winner was one of the simplest, TIT FOR TAT.

Axelrod then constructed an environment in which different programs competed, and the losing programs were eliminated: this was an ecology that rewarded high scoring programs, and punished others. "This process simulates survival of the fittest. A rule that is successful on average with the current distribution of rules in the population will become an even larger proportion of the environment of the other rules in the next generation. At first, a rule that is successful with all sorts of rules will proliferate, but later as the unsuccessful rules disappear, success requires good performance with other successful rules." In other words, the competition gets tougher.

"The analysis of the tournament results indicate that there is a lot to be learned about coping in an environment of mutual power. Even expert strategists from political science, sociology, economics, psychology, and mathematics made the systematic errors of being too competitive for their own good, not being forgiving enough, and being too pessimistic about the responsiveness of the other side."

The tournaments reveal that "there is a single property which distinguishes the relatively high-scoring entries from the relatively low-scoring entries. This is the property of being nice, which is to say never being the first to defect."

TIT FOR TAT's rules for success:

  • Be nice. Don't be the first to go on the attack. This demonstrates good will, and avoids provoking others.
  • Retaliate. If others attack, retaliate. Not doing so encourages bad behavior and gives niceness a bad reputation.
  • Be forgiving. If others defect but then go back to cooperating, accept the opportunity to move back to a cooperative mode.
  • Be clear. Others can predict what you'll do, be certain that their moves will have definite outcomes. "There is an important contrast between a zero-sum game like chess and a non-zero-sum game like the iterated PD. In chess, it is useful to keep the other player guessing about your intentions. The more the other player is in doubt, the less efficient will be his or her strategy. But in a non-zero-sum setting it does not always pay to be so clever. In the iterate PD, you benefit from the other player's cooperation."

Chapter 4, Trench Warfare. During World War I, "live and let live" arrangements emerged spontaneously between opposing units on the Western Front. Cooperation could take hold because "the same small units faced each other in immobile sectors for extended periods of time." Consequently, they had a more sustained relationship than in mobile warfare, and could develop commonly-understood rules, reciprocity and restraint in attacks, displays of strength (e.g., snipers shooting at hard targets)as well as ethics (recognition that there was an arrangement and violating it was immoral) and rituals (e.g., regular artillery firing).

"Cooperation first emerged spontaneously in a variety of contexts, such as restraint in attacking the distribution of enemy rations, a pause during the first Christmas in the trenches, and a slow resumption of fighting after bad weather made sustained combat almost impossible. These restraints quickly evolved into clear patterns of mutually understood behavior, such as two-for-one or three-for-one retaliation for actions that were taken to be unacceptable."

Chapter 6, How to Choose Effectively. Four suggestions about how to do well in PD:

  • Don't be envious. In a PD, "envy is self-destructive. Asking how well you are doing compared to how well the other player is doing is not a good standard unless your goal is to destroy the other player." However, in an iterated prisoner's dilemma, you can't do better than the other player, unless they're always suckers. "In a non-zero-sum world you do not have to do better than the other player to do well for yourself. The other's success is virtually a prerequisite of your doing well for yourself."
  • Don't be the first to defect (be nice). "It pays to cooperate as long as the other player is cooperating." In a short game, defection can make sense; but in a relationship, taking advantage of the other person is self-defeating.
  • Reciprocate both cooperation and defection. TIT FOR TAT "does not destroy the basis of its own success. On the contrary, it thrives on interactions with other successful rules." However, the right level of forgiveness depends on the context, and the other players' strategies.
  • Don't be too clever. "In a zero-sum game, such as chess it pays for us to be as sophisticated and as complex in our analysis as we can. Non-zero-sum games are not like this. The other player can respond to your own choices. And unlike the chess opponent, the other player in a PD should not be regarded as someone who is out to defeat you." "There is an important contrast between a zero-sum game like chess and a non-zero-sum game like the iterated PD. In chess, it is useful to keep the other player guessing about your intentions. The more the other player is in doubt, the less efficient will be his or her strategy. But in a non-zero-sum setting it does not always pay to be so clever. In the iterate PD, you benefit from the other player's cooperation."

Chapter 7, How to Promote Cooperation. Promoting cooperation can be thought of as an exercise in tinkering with the variables in a PD. "As long as the interaction is not iterated, cooperation is very difficult. That is why an important way to promote cooperation is to arrange that the same two individuals will meet each other again, be able to recognize each other from the past, and to recall how the other has behaved until now."

  • Enlarge the shadow of the future. For cooperation to emerge, players must be in a continuing relationship, with the expectation that it will continue in the future. "Mutual cooperation can be stable if the future is sufficiently important relative to the past." "There are two basic ways of doing this: by making the interactions more durable, and by making them more frequent. [P]rolonged interaction allows patterns of cooperation which are based on reciprocity to be worth trying and allows them to become established," Making interactions more frequent makes "the next interaction occur sooner, and hence the next move looms larger than it otherwise would." You might do this by enforcing isolation, or constructing hierarchies or organizations, which are "especially effective at concentrating the interactions between specific individuals."
  • Change the payoffs. Make defection less attractive, by enforcing laws, or growing the value of long-term incentives.
  • Teach people to care about each other.
  • Teach reciprocity. Reciprocity "actually helps not only oneself, but others as well. It helps others by making it hard for exploitative strategies to survive."
  • Improve recognition abilities. "The ability to recognize the other player from past interactions, and to remember the relevant features of those interactions, is necessary to sustain cooperation. Without these abilities, a player could not use any form of reciprocity and hence could not encourage the other to cooperate."

Chapter 8, The Social Structure of Cooperation.
The social structure of cooperation involves labels, reputation, regulation, and territoriality.

  • Labels are fixed characteristics of an agent that are observable by other agents. Labels affect reciprocity and retaliation via assumptions of group similarity and stereotypes.
  • Reputation is others' belief about the strategies an agent will employ. Reputation may be based on past behavior or on rumours, i.e. reputation can be accurate or merely believed. Reputation affects whether or not other agents will cooperate or defect with you.
  • Regulation involves setting the stringency of a standard of behavior "high enough to get most of the social benefits of regulation, and not so high as to prevent the evolution of a stable pattern of voluntary compliance from almost all of the companies" (or regulated agents).
  • Territoriality refers to both physical and conceptual spaces that can be 'invaded' by agents of differing strategies. Territoriality establishes boundaries within which behaviors will be reinforced or retaliated against depending on prevailing norms. Also, the boundary provides an 'inside' for agents that comply with the norms, and an 'outside' to which they can be expelled if they do not comply.

Chapter 9, The Robustness of Reciprocity.

  • Cooperation can get started by even a small cluster of individuals who are willing to reciprocate cooperation, even in a world where no one else will cooperate.
  • Once cooperation is establish, it protects itself from invasion by non-cooperative strategies.
  • The foundation of cooperation is the durability of the relationship, which allows agents to learn about each other in order to cooperate.

Swarm Smarts

One Sentence Summary:
Insect studies on emergent intelligence in swarms of unintelligent actors has practical relevance to distributed computing, robotics, and other applications; for example, foraging insects use pheromone trails to select the shortest paths to food, a strategy that has been used to solve the famous "traveling salesman problem" in computer science.
Disciplines:
Biology
Computer Science
Findings:
  • Intelligence can be an emergent property resulting from the cooperative dynamics of distributed simple individuals. “Dumb” parts connected properly can yield smart results.
  • When intelligence is distributed across a network of individuals, then the system as a whole is better able to adapt well to changing environments, and it becomes robust at dealing with damage.
Keywords:
agent-based model
complexity
evolution
Author(s) / Editor(s):
Published in:
Scientific American
Date:
March 2000
One Paragraph Summary:

Insect studies on emergent intelligence in swarms of unintelligent actors has practical relevance to distributed computing, robotics, and other applications; for example, foraging insects use pheromone trails to select the shortest paths to food, a strategy that has been used to solve the famous "traveling salesman problem" in computer science. Systems with distributed collective intelligence are more robust because they can adapt quickly to a variety of situations.

One Page Summary:

Insect studies on emergent intelligence in swarms of unintelligent actors has practical relevance to distributed computing, robotics, and other applications; for example, foraging insects use pheromone trails to select the shortest paths to food, a strategy that has been used to solve the famous "traveling salesman problem" in computer science. Systems with distributed collective intelligence are more robust because they can adapt quickly to a variety of situations.

Foraging ants select the shortest paths to food. They are so efficient that ant models have been used to solve the famous “traveling salesmen problem,” a classic in computer science, which concerns finding the shortest route that will take a salesman through a group of cities. Successive iterations over path networks (paths that have been discovered) results in the shortest routes getting reinforced and the longest ones getting abandoned. The outcome is an optimal path length for ant foraging.

Also, artificial ants provide the best solution to the classic quadratic assignment problem, in which the manufacture of a number of goods must be assigned to different factories so as to minimize the total distance over which the items need to be transported between facilities. There exist many such “optimization problems”, such as telephone routing. Also, individual robots have been programmed to push a box to a destination without communicating.

In another project, a model that was initially introduced to explain how ants cluster their dead and sort their larvae has become the basis of a new approach for analyzing financial data. “The ant-based approach enables the data to be visualized easily, and it boasts one intriguing feature: the number of clusters emerges automatically from the data, whereas conventional methods usually assume a predefined number of groups into which the data are then fit. Thus, antlike sorting has been effective in discovering interesting commonalties that might otherwise have remained hidden.”

Again using a biological system as a model, scientists have devised a technique for scheduling paint booths in a truck factory. The method optimizes variables like paint usage and time spent, as well as implementing load-sharing between paint booths in the case of breakdowns.

“Indeed, the potential of swarm intelligence is enormous. It offers an alternative way of designing systems that have traditionally required centralized control and extensive preprogramming. It instead boasts autonomy and self-sufficiency, relying on direct or indirect interactions among simple individual agents. Such operations could lead to systems that can adapt quickly to rapidly fluctuating conditions.”

How To Cope With Noise in the Iterated Prisoner's Dilemma

One Sentence Summary:
The Tit-for-Tat strategy is vulnerable to noise – errors in implementing choices – that can lead to echoing defections, but can be made less sensitive by adding generosity (occasionally refraining from punishing defection by opponent) and contrition (refraining from punishing a reaction to accidental defection.)"
Disciplines:
Biology
Computer Science
Economics
Political Science
Findings:
  • Random errors in implementing strategies is common in the real world ("noise"), and Tit-for-Tat is sensitive to noise because echoes of a mistake (a defection that was meant to be a cooperation) can continue indefinitely.
  • An article in Nature, 1993 (Nowak& Sigmund, "Strategy of Win-Stay, Lose Shift That Outperforms Tit-for-Tat," 364: 56-58) highlighted a strategy that also applies to real-world situations – defectors can shift partners until they find those that are exploitable, and cooperators can shift partners until they find co-cooperators.
Keywords:
agent-based model
complexity
cooperation
game theory
reciprocity
tit-for-tat
prisoners dilemma
Author(s) / Editor(s):
Published in:
Journal of Conflict Resolution 39, No. 1: 183-189
Date:
March 1995
One Paragraph Summary:

Axelrod became concerned with the problem of noise – mistaken defections in Prisoner's Dilemma games that can lead to echoing repetitions – during the Cuban Missile crisis. Adding generosity and contrition to Tit-for-Tat and reimplementing the 63 rules of the original iterated Prisoner's Dilemma tournament proved to be an effective way of coping with noise; Win-Stay, Lose-Shift did not do as well in such an environment. Axelrod was able to put Soviet and US nuclear strategists together to play Prisoner's Dilemma in 1988 for an audience of social scientists -- with noise deliberately introduced. This tournament was the basis for Axelrod's statement that "Noise calls for forgiveness, but too much forgiveness invites exploitation." The authors also noted: "Generosity can correct an error by either player, but contrition can only correct one's own error. Thus, when the population of strategies one is likely to meet has not adapted to the presence of noise, a strategy like Generous Tit-for-Tat is likely to be effective. On the other hand, if the strategies of the other players one is likely to meet have already adapted to noise, then a strategy like Contrite Tit-for-Tat is likely to be even more effective because it can correct its own errors and restore mutual cooperation almost immediately."

Gregor Mendel, Meet Florence Nightingale: Summaries and Findings

One Sentence Summary:
Inspection of the genetic relatedness of two groups of rice farmers, one whose circumstances necessitated cooperation, and another group of hillside farmers whose agricultural practices enabled more independence, probed for evidence of how "ecological feedback can influence social structure, and note how these processes leave recoverable traces in population genetic structure."
Disciplines:
Biology
Anthropology
Cultural Evolution
Computer Science
Political Science
Psychology
Findings:
  • This is an example of interdisciplinary research capable of probing the complexities of human cooperation, using linguists, geneticists, anthropologists and computer scientists to examine the interactions among environmental circumstances, biological relationships, and cultural practices.
  • Settled agriculturists whose irrigation needs require the cooperative creation of public goods associated with a fixed territory tend to intermarry more than agriculturists who do not use large-scale irrigation and who move their plots from time to time. Although simple, this is a good example of the coevolution of cultural and biological aspects of human group behavior.
Keywords:
cooperation
cultural evolution
evolution
agent-based model
Author(s) / Editor(s):
Published in:
Santa Fe Institute Bulletin, vol. 20, no. 1
Date:
Spring, 2005
One Paragraph Summary:

Comparisons of the genetic relatedness of two populations enable the kind of multidisciplinary convergence required for cooperation studies: University of Arizona professor of anthropology Stephen Lansing, after thirty years of study in Indonesia, teamed up with Santa Fe Institute colleagues to "build a new microscope and aim it at the emergence of patterns of social structure through time." Population genetics showed that lowland farmers who had to stay in one place and work cooperatively with neighbors to maintain shared irrigation resources were more closely genetically related than highland rice farmers who had less permanent connections to particular farmlands and to their neighbors. An observed difference in genetic relatedness between two culturally similar groups whose circumstances required different degrees of cooperation can be explained by a wide variety of factors, including "marriage rules, migration, language drift, historical changes in modes of production. Lansing et. al. used agent-based modeling to "simulate what might have led up to the patterns we see in the data."

Evolution of Indirect Reciprocity

One Sentence Summary:
Cooperation through indirect reciprocity, captured by the phrase "I help you, someone else helps me", requires the evolution of reputations and communication of those reputations among the larger group (as in the human instinct to gossip), cognitive abilities beyond being able to identify relatives (required for kin selection) or the individuals who have cooperated with you in the past (required for direct reciprocity).
Disciplines:
Economics
Sociology
Psychology
Findings:
  • "The hypothesis that more information leads to more cooperation has been confirmed in experiments, which compare three information conditions. In one condition, players have no information about their co-players; in the second they are told about what their co-players have decided when last in the role of a donor; and in the third they also know about the score of the recipient of the co-player. We note that this is not always enough to decide whether a previous defection was justified or not. However, the additional knowledge did enhance cooperation."
  • "Indirect reciprocity is situated somewhere between direct reciprocity and public goods. On the one hand it is a game between two players only, the donor and the recipient, but on the other hand it has to be played within a larger group. Richard Alexander claimed that indirect reciprocity originates from direct reciprocity in the presence of interested audiences."
  • "It is easy to conceive that an organism experiences as 'good' or 'bad' anything that affects the organism's own reproductive fitness in a positive or negative sense. The step from there to judging, as 'good' or 'bad', actions between third parties, is not obvious. The same terms 'good' and 'bad' that are applied to pleasure and pain are also used for moral judgements: this linguistic quirk reveals an astonishing degree of empathy, and reflects highly developed faculties for cognition and abstraction."
  • Even a group of players with discriminating strategies can be sidetracked by imperfect transfer of reputation information, as in unfounded rumors or exaggeration: "if players have different views about the reputation of others, then errors in perception can undermine cooperation."
  • In empirical studies, discriminating players are sensitive to their own score: "players who justifiably refuse to donate to a defector show an increased tendency to provide donations in the following round, as if to make up for that refusal. This indicates that they expect their refusal to lower their score in the co-players' eyes and that they do not rely on the community's understanding."
Keywords:
agent-based model
altruism
assurance game
communication
cooperation
equilibrium
game theory
language
norms
prisoners dilemma
public goods
punishment
reciprocity
reputation
tit-for-tat
trust
Author(s) / Editor(s):
Published in:
Nature 437, 1291-1298
Date:
October 27, 2005
One Paragraph Summary:

Cooperation through indirect reciprocity, can be captured by the phrase "I help you, someone else helps me". Indirect reciprocity helps explain how cooperation is possible at all when economic transactions move beyond small villages where one can easily keep track of one's interactions with everyone else. The success of strategies of indirect reciprocity in empirical studies might be attributable to the fact that humans care so deeply not only about how they are treated, but about the results of interactions between third parties. This concern and the desire to communicate concerns, or gossip, might in turn be explained by evolutionary psychology and the benefits of cooperation in large groups, surpluses resulting from division of labor. To test strategies of indirect reciprocity no two players can interact more than once and the scores of players (the portion of times they have cooperated with others) must be visible. A player choosing a simple version of indirect reciprocity will only cooperate with those whose score is above a certain threshold. However, this player might be punishing another player using indirect reciprocity who has only interacted with defectors. "Effectively, discriminating players pay a cost for punishing bad co-players. Such a form of altruistic punishment can promote cooperation in the community, but at a cost to the punisher, and thus can be viewed as a social dilemma." A more sophisticated strategy would have a player discriminate between justified defection (defecting to punish someone who always defects) and unjustified defection (defecting regardless of the recipients reputation). This strategy avoids the case where a group of players who always cooperate is invaded by a group of players who always defect, but it requires the cognitive abilities to keep track of interactions that are far removed from one's own.

Bandwidth and Echo: Trust, Information, And Gossip in Social Networks

One Sentence Summary:
Network closure produces echo, gossip that reinforces dispositions rather than increasing information flow or the kind of trust that increases social capital.
Disciplines:
Business
Sociology
Information
Findings:
  • Bandwidth hypothesis: "The bandwidth prediction is that ego's opinion of alter is correlated with third-party opinion, and networks evolve toward a state of balance in which people bound by a strong relationship have similar opinions of others."
  • Echo hypothesis: "Echo results from etiquette biasing the information that third parties disclose to ego. [...] The echo prediction is that stronger third-party ties foster more intense ego opinion such that relations adjacent in a network need not be balanced in their direction (I trust friends of my friends), so much as their intensity (I have an opinion, positive or negative, of my friends' friends)"
Keywords:
trust
group forming networks
social capital
networks
complexity
communication
agent-based model
Source:
Edited by Alessandra Casella and James E. Rauch, Russell Sage Foundation
Author(s) / Editor(s):
Published in:
Pre-print for a chapter in Networks and Markets: Contributions from Economics and Sociology
Date:
2001
One Paragraph Summary:

The competitive advantage that social networks create is called social capital. Empirical evidence shows that brokerage between interdependent groups that specialize on different things creates more social capital than simply a high number of relationships among individuals (i.e. network closure). However, brokers depend on trust, and trust is frequently viewed to require network closure. The problem with this view is that with increased network closure the value of brokers diminishes which in turn creates less social capital. Part of solving this problem is to figure out whether network closure really does produce the kind of trust that increases social capital. Burt shows that trust created by network closure might be ill-founded.

One Page Summary:

The competitive advantage that social networks create is called social capital. Empirical evidence shows that brokerage between interdependent groups that specialize on different things creates more social capital than simply a high number of relationships among individuals (i.e. network closure). However, brokers depend on trust, and trust is frequently viewed to require network closure. The problem with this view is that with increased network closure the value of brokers diminishes which in turn creates less social capital. Part of solving this problem is to figure out whether network closure really does produce the kind of trust that increases social capital. Burt shows that trust created by network closure might be ill-founded.

The relationship strength between ego and alter correlates with the amount of trust between ego and alter. In a social context ego also receives gossip about alter, i.e. information about alter via third parties. The bandwidth hypothesis states that gossip nework closure increases information flow reinforcing and fine-tuning trust relationships beneficial to social capital. The echo hypothesis states that gossip network closure does not so much increase information flow but reinforces dispositions. This is due to a commonly observed etiquette in informal conversations where third parties only reveal information about alter to ego that concur with ego's opinion of alter. The motivation for this etiquette are civility, efficiency, and the important role gossip plays in creating and maintaining relationships.

Analysis of survey network data of three study populations consisting of senior managers in a leading manufacturer of electronic components and computer equipment, of staff officers in two financial companies, and a bankers in the investment banking division of a large financial company shows that trust can develop within negative third-party ties ("an enemy of my friend is my enemy" or "a friend of my enemy is my enemy"), and distrust can develop within positive third-party ties ("a friend of my friend is my friend" or "an enemy of my enemy is my enemy") which is consistent with the echo hypothesis but not with the bandwidth hypothesis.

"Strong connection through third parties increases the probability of social reinforcement such that network closure creates echo, not accuracy. [...] Therefore, network closure does not facilitate trust so much as it amplifies dispositions, people cannot learn of what they do not already know" which negatively impacts social capital.

An Evolutionary Approach to Norms

One Sentence Summary:
Exploration of games in which punishment is possible and cheating is not automatically detected reveals that norms can emerge and stabilize only if those who fail to punish violators are also punished.
Disciplines:
Biology
Computer Science
Economics
Political Science
Findings:
  • Norms can emerge in competitive situations when players can observe each other and imitate the strategies of successful players.
  • N-person Prisoner's Dilemma games can't be resolved with simple reciprocity without enabling cooperators to also punish defectors.
  • Norms can emerge and grow stable if metanorms establish a willingness to not only punish violators but also those who fail to punish violators.
  • Norms likely emerge from behaviors that signal others to reward individuals (reputation), and spread through both imitation as well as punishment of violators.
  • "There may be some useful cooperative norms that could be hurried along with relatively modest interventions."
Keywords:
reputation
cooperation
evolution
norms
game theory
agent-based model
cultural evolution
complexity
competition
prisoners dilemma
altruism
Author(s) / Editor(s):
Published in:
American Political Science Review 80, No. 41095-1111
Date:
1997
One Paragraph Summary:

The decrease in punishment of those who failed to punish violators may have played a part in the sudden collapse of communism, and Granovetter noted that riots can have tipping points in which "a slight change in the willingness of a few people to act first can get the ball rolling." Axelrod defines norms thus: "A norm exists in a given social setting to the extent that individuals usually act in a certain way and are often punished when seen not to be acting in this way." Therefore, norms are a matter of degree, not all or nothing. "By linking vengefulness against nonpunishers with vengefulness against defectors, the metanorm provides a mechanism by which the norm against defection becomes self-policing." Reputation plays a role because defection is not only a means for a defector to harvest a payoff, but a signal that can be used be others: "a norm is likely to originate in a type of behavior that signals things about individuals that will lead others to reward them." The observation from norms-game trials that norms can sometimes establish themselves quickly led Axelrod to conclude that "there may be some useful cooperative norms that could be hurried along with relatively modest interventions."

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