2,255 research outputs found

    Global adaptation in networks of selfish components: emergent associative memory at the system scale

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    In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

    Cooperation in effective action

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    The ability to cooperate in collective action problems — such as those relating to the use of common property resources or the provision of local public goods — is a key determinant of economic performance. In this paper we discuss two aspects of collective action problems in developing countries. First, which institutions discourage opportunistic behavior and promote cooperation? Second, what are the characteristics of the individuals involved that determine the degree to which they cooperate? We first review the evidence from field studies, laboratory experiments, and cross community studies. We then present new results from an individual level panel data set of rural workers

    The evolution of cooperative norms: evidence from a natural field experiment

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    We document the establishment and evolution of a cooperative norm among workers using evidence from a natural field experiment on a leading UK farm. Workers are paid according to a relative incentive scheme under which increasing individual effort raises a worker's own pay but imposes a negative externality on the pay of all co-workers, thus creating a rationale for cooperation. As a counterfactual, we analyze worker behavior when workers are paid piece rates and thus have no incentive to cooperate. We find that workers cooperate more as their exposure to the relative incentive scheme increases. We also find that individual and group exposure are substitutes, namely workers who work alongside colleagues with higher exposure cooperate more. Shocks to the workforce in the form of new worker arrivals disrupt cooperation in the short term but are then quickly integrated into the norm. Individual exposure, group exposure, and the arrival of new workers have no effect on productivity when workers and paid piece rates and there is no incentive to cooperate

    Linear quadratic mean-field game-team analysis: a mixed coalition approach

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    Mean-field theory has been extensively explored in decision analysis of {large-scale} (LS) systems but traditionally in ``pure" cooperative or competitive settings. This leads to the so-called mean-field game (MG) or mean-field team (MT). This paper introduces a new class of LS systems with cooperative inner layer and competitive outer layer, so a ``mixed" mean-field analysis is proposed for distributed game-team strategy. A novel asymptotic mixed-equilibrium-optima is also proposed and verified

    Random mobility and spatial structure often enhance cooperation

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    The effects of an unconditional move rule in the spatial Prisoner's Dilemma, Snowdrift and Stag Hunt games are studied. Spatial structure by itself is known to modify the outcome of many games when compared with a randomly mixed population, sometimes promoting, sometimes inhibiting cooperation. Here we show that random dilution and mobility may suppress the inhibiting factors of the spatial structure in the Snowdrift game, while enhancing the already larger cooperation found in the Prisoner's dilemma and Stag Hunt games.Comment: Submitted to J. Theor. Bio
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