SMU SOE Seminar (Dec 7, 2017): Self-fulfilling Equilibrium in Social Contests: Expectation and Neighborhood Effect

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TOPIC:

SELF-FULFILLING EQUILIBRIUM IN SOCIAL CONTESTS: EXPECTATION AND NEIGHBORHOOD EFFECT

ABSTRACT We consider the following social contest model. In our model, individuals share a resource proportionally to their efforts; and efforts are costly. However, individuals, located in a network, are locally-sighted. That is, each individual only perceives the resource in his neighborhood and responds to the efforts of his neighbors. Local-sightedness thus reflects a constraint neighborhood imposes on its residents. To capture plausible outcomes of the model, we propose a new equilibrium concept: self-fulfilling equilibrium. A self-fulfilling equilibrium consists of a share vector and an effort vector: given the total share of his neighborhood, an individual’s effort is an optimal response to the efforts of his neighbors; and given the effort vector, an individual’s share is proportional to his effort (and consequently, the share of any neighborhood is proportional to the total efforts of the neighborhood). We show that if effort costs the same across individuals, in any equilibrium, an individual with higher expectation, i.e., exposed to neighbors of higher shares, exerts higher effort and obtains higher share himself. In particular, if the network is strongly connected and the cost is linear, then the (unique) equilibrium effort vector is proportional to the eigenvector centrality of the network. We then look at the case when costs are different, and show how the advantage of lower cost may be offset, and the disadvantage of higher cost may be compensated by different network structures. Click here to view his CV.
		PRESENTER Yan Long NYU Abu Dhabi
		RESEARCH FIELDS Microeconomic Theory Mechanism Design Social Choice Game Theory
		DATE: 7 December 2017 (Thursday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903

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