Lightweight Interactions for Reciprocal Cooperation in a Social Network Game

The construction of reciprocal relationships requires cooperative interactions during the initial meetings. However, cooperative behavior with strangers is risky because the strangers may be exploiters. In this study, we show that people increase the likelihood of cooperativeness of strangers by using lightweight non-risky interactions in risky situations based on the analysis of a social network game (SNG). They can construct reciprocal relationships in this manner. The interactions involve low-cost signaling because they are not generated at any cost to the senders and recipients. Theoretical studies show that low-cost signals are not guaranteed to be reliable because the low-cost signals from senders can lie at any time. However, people used low-cost signals to construct reciprocal relationships in an SNG, which suggests the existence of mechanisms for generating reliable, low-cost signals in human evolution.Comment: 13 pages, 2 figure

The interaction effect of relational norms and agent cooperativeness on opportunism in buyerâ supplier relationships

In this study, we examined the effect of relational norms and agent cooperativeness on opportunism in buyerâ supplier relationships. Drawing from the theoretical grounding of transaction cost economics, personality trait theory, and contingency theory, we proposed three distinct perspectives on opportunism mitigation in buyerâ supplier relationships: (1) organizationalist, (2) individualist, and (3) interactionist, where relational norms, agent cooperativeness, and the interaction between them, respectively, serve as the key predictors in these three perspectives. The results of replicated experiments indicated that relational norms and agent cooperativeness interact with each other in mitigating opportunism and that the interactionist perspective yielded the highest explained variance in opportunism. This suggests that the interactionist perspective, a multiâ level theoretical lens encompassing the dynamic interplay between organizationâ level and individualâ level factors, was a more complete model in explaining opportunism than either the organizationalist or individualist perspectives. The consensus which emerged from postâ experimental interviews of purchasing professionals is that agent personalities play an important role in buyerâ supplier relationships. Some purchasing professionals had observed that uncooperative agents or personnel turnover in the boundaryâ spanning functions can substantially undermine even established relational exchanges. These qualitative findings are in line with our theoretical arguments and experimental outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146947/1/joom398.pd

Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization

We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON

The resurrection of group selection as a theory of human cooperation

Two books edited by members of the MacArthur Norms and Preferences Network (an interdisciplinary group, mainly anthropologists and economists) are reviewed here. These books in large part reflect a renewed interest in group selection that has occurred among these researchers: they promote the theory that human cooperative behavior evolved via selective processes which favored biological and/or cultural group-level adaptations as opposed to individual-level adaptations. In support of this theory, an impressive collection of cross-cultural data are presented which suggest that participants in experimental economic games often do not behave as self-interested income maximizers; this lack of self-interest is regarded as evidence of group selection. In this review, problems with these data and with the theory are discussed. On the data side, it is argued that even if a behavior seems individually-maladaptive in a game context, there is no reason to believe that it would have been that way in ancestral contexts, since the environments of experimental games do not at all resemble those in which ancestral humans would have interacted cooperatively. And on the theory side, it is argued that it is premature to invoke group selection in order to explain human cooperation, because more parsimonious individual-level theories have not yet been exhausted. In summary, these books represent ambitious interdisciplinary contributions on an important topic, and they include unique and useful data; however, they do not make a convincing case that the evolution of human cooperation required group selection

The Viability of Cooperation Based on Interpersonal Commitment

A prominent explanation of cooperation in repeated exchange is reciprocity (e.g. Axelrod, 1984). However, empirical studies indicate that exchange partners are often much less intent on keeping the books balanced than Axelrod suggested. In particular, there is evidence for commitment behavior, indicating that people tend to build long-term cooperative relationships characterised by largely unconditional cooperation, and are inclined to hold on to them even when this appears to contradict self-interest. Using an agent-based computational model, we examine whether in a competitive environment commitment can be a more successful strategy than reciprocity. We move beyond previous computational models by proposing a method that allows to systematically explore an infinite space of possible exchange strategies. We use this method to carry out two sets of simulation experiments designed to assess the viability of commitment against a large set of potential competitors. In the first experiment, we find that although unconditional cooperation makes strategies vulnerable to exploitation, a strategy of commitment benefits more from being more unconditionally cooperative. The second experiment shows that tolerance improves the performance of reciprocity strategies but does not make them more successful than commitment. To explicate the underlying mechanism, we also study the spontaneous formation of exchange network structures in the simulated populations. It turns out that commitment strategies benefit from efficient networking: they spontaneously create a structure of exchange relations that ensures efficient division of labor. The problem with stricter reciprocity strategies is that they tend to spread interaction requests randomly across the population, to keep relations in balance. During times of great scarcity of exchange partners this structure is inefficient because it generates overlapping personal networks so that often too many people try to interact with the same partner at the same time.Interpersonal Commitment, Fairness, Reciprocity, Agent-Based Simulation, Help Exchange, Evolution

Why and How Identity Should Influence Utility

This paper provides an argument for the advantage of a preference for identity-consistent behaviour from an evolutionary point of view. Within a stylised model of social interaction, we show that the development of cooperative social norms is greatly facilitated if the agents of the society possess a preference for identity consistent behaviour. As cooperative norms have a positive impact on aggregate outcomes, we conclude that such preferences are evolutionarily advantageous. Furthermore, we discuss how such a preference can be integrated in the modelling of utility in order to account for the distinctive cooperative trait in human behaviour and show how this squares with the evidence

What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?

In an earlier experiment, participants played a perfect information game against a computer, which was programmed to deviate often from its backward induction strategy right at the beginning of the game. Participants knew that in each game, the computer was nevertheless optimizing against some belief about the participant's future strategy. In the aggregate, it appeared that participants applied forward induction. However, cardinal effects seemed to play a role as well: a number of participants might have been trying to maximize expected utility. In order to find out how people really reason in such a game, we designed centipede-like turn-taking games with new payoff structures in order to make such cardinal effects less likely. We ran a new experiment with 50 participants, based on marble drop visualizations of these revised payoff structures. After participants played 48 test games, we asked a number of questions to gauge the participants' reasoning about their own and the opponent's strategy at all decision nodes of a sample game. We also checked how the verbalized strategies fit to the actual choices they made at all their decision points in the 48 test games. Even though in the aggregate, participants in the new experiment still tend to slightly favor the forward induction choice at their first decision node, their verbalized strategies most often depend on their own attitudes towards risk and those they assign to the computer opponent, sometimes in addition to considerations about cooperativeness and competitiveness.Comment: In Proceedings TARK 2017, arXiv:1707.0825

Agent Decision-Making in Open Mixed Networks

Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.Engineering and Applied Science

The Effect of Friendship on Decisions: Field Studies of Real Estate Transactions

A field study of real estate agents\u27 transactions demonstrates that business friendship affects the negotiation process and the outcome of transactions more for agents with 10 or more years of experience in real estate brokerage than for less experienced agents. Newer agents rely more on friendly relations and on impression management techniques to do well in a transaction, while the more experienced agents consider genuine business friendship and its norms useful for facilitating transactions. Different scripts for friendship may explain the consistency within each of the two groups