Human aggression (Part 3)

This theory sees aggression as social learning. According to social learning theories in earlier studies, people acquire aggressive responses the same way they acquire other complex forms of social behavior which either by direct experience or by observing others. Social learning theory explains the acquisition of aggressive behaviors, via observational learning processes, and provides a useful set of concepts for understanding and describing the beliefs and expectations that guide social behavio

Social Interaction, Observational Learning, and Privacy: the "Do Not Call" Registry

Many empirical studies have inferred contagion in behavior from a correlation between individual behavior and the behavior of others in the same social group, rather than from any direct evidence. The correlation has been variously attributed to social interaction, word of mouth communication, and observational learning. As Manski (1993) famously observed, such correlation might be explained by peer group influence, but also, similar responses to common environmental changes. More generally, correlation in behavior raises two questions – how information is transmitted and why individuals follow the choices of others. We address these questions in the context of subscriptions to the U.S. "do not call" registry in June-August 2003. Using a rich set of data culled from multiple sources, including longitudinal observations of household choice, we are able to separately identify -- Methods by which information is transmitted – social interaction and news media; -- Reasons why households follow the choices of others – observational learning and telemarketing diversion, and the impact of household heterogeneity on such learning and diversion. Among methods of information transmission, social interaction was relatively more important than news media. Among reasons for contagion, telemarketing diversion was relatively more important than observational learning, while the extent of learning decreased with social heterogeneity.

Social Learning with Payoff Complementarities

We incorporate strategic complementarities into a multi-agent sequential choice model with observable actions and private information. In this framework agents are concerned with learning from predecessors, signalling to successors, and coordinating their actions with those of others. Coordination problems have hitherto been studied using static coordination games which do not allow for learning behavior. Social learning has been examined using games of sequential action under uncertainty, but in the absence of strategic complementarities (herding models). Our model captures the strategic behavior of static coordination games, the social learning aspect of herding models, and the signalling behavior missing from both of these classes of models in one unified framework. In sequential action problems with incomplete information, agents exhibit herd behavior if later decision makers assign too little importance to their private information, choosing instead to imitate their predecessors. In our setting we demonstrate that agents may exhibit either strong herd behavior (complete imitation) or weak herd behavior (overoptimism) and characterize the informational requirements for these distinct outcomes. We also characterize the informational requirements to ensure the possibility of coordination upon a risky but socially optimal action in a game with finite but unboundedly large numbers of players.

From the social learning theory to a social learning algorithm for global optimization

Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization approach that mimics the social learning process of humans. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Experimental studies in this paper showed the appealing search behavior of this human intelligence-inspired approach, which can reach the global optimum even in ill conditions. The effectiveness and high efficiency of the proposed algorithm has further been verified by comparing to some representative EC algorithms and variants on a set of benchmarks