18,931 research outputs found
Network formation by reinforcement learning: the long and medium run
We investigate a simple stochastic model of social network formation by the
process of reinforcement learning with discounting of the past. In the limit,
for any value of the discounting parameter, small, stable cliques are formed.
However, the time it takes to reach the limiting state in which cliques have
formed is very sensitive to the discounting parameter. Depending on this value,
the limiting result may or may not be a good predictor for realistic
observation times.Comment: 14 page
Decision-Making: A Neuroeconomic Perspective
This article introduces and discusses from a philosophical point of view the nascent field of neuroeconomics, which is the study of neural mechanisms involved in decision-making and their economic significance. Following a survey of the ways in which decision-making is usually construed in philosophy, economics and psychology, I review many important findings in neuroeconomics to show that they suggest a revised picture of decision-making and ourselves as choosing agents. Finally, I outline a neuroeconomic account of irrationality
Multi-round Master-Worker Computing: a Repeated Game Approach
We consider a computing system where a master processor assigns tasks for
execution to worker processors through the Internet. We model the workers
decision of whether to comply (compute the task) or not (return a bogus result
to save the computation cost) as a mixed extension of a strategic game among
workers. That is, we assume that workers are rational in a game-theoretic
sense, and that they randomize their strategic choice. Workers are assigned
multiple tasks in subsequent rounds. We model the system as an infinitely
repeated game of the mixed extension of the strategic game. In each round, the
master decides stochastically whether to accept the answer of the majority or
verify the answers received, at some cost. Incentives and/or penalties are
applied to workers accordingly. Under the above framework, we study the
conditions in which the master can reliably obtain tasks results, exploiting
that the repeated games model captures the effect of long-term interaction.
That is, workers take into account that their behavior in one computation will
have an effect on the behavior of other workers in the future. Indeed, should a
worker be found to deviate from some agreed strategic choice, the remaining
workers would change their own strategy to penalize the deviator. Hence, being
rational, workers do not deviate. We identify analytically the parameter
conditions to induce a desired worker behavior, and we evaluate experi-
mentally the mechanisms derived from such conditions. We also compare the
performance of our mechanisms with a previously known multi-round mechanism
based on reinforcement learning.Comment: 21 pages, 3 figure
Stakeholder engagement as a facilitator of organizational learning
This paper examines the relationship between stakeholder engagement and competence building. Following the dual perspective of the firm, which indicated that managers deal with both transactions and competences concurrently, we argue that stakeholder interactions also concern both transaction cost reduction and value creation. Based on a review of the extant literature, we incorporated a micro-macro connection between organizational learning and competence building. Further to this, we developed a conceptual framework by linking stakeholder engagement and organizational learning. This framework demonstrates that stakeholder relations may have significant effects on organizational learning and thus stakeholder engagement can play the role of facilitator in building firm competences
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