A Structural Model for the Coevolution of Networks and Behavior

König, Michael; Hsieh, Chih‐Sheng; Liu, Xiaodong

doi:10.1162/rest_a_00958

Cited by 15 publications

(10 citation statements)

References 65 publications

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“…We prove this proposition by applying a combination of the arguments of [11,27]; in particular, players revise both links and actions, as in the second paper, but differently from there, link formation is one-sided as in the first paper. Some remarks are in order.…”

Section: The Steady State Distributionmentioning

confidence: 86%

“…The co-evolution of networks and behavior is a stochastic best-response dynamic similar to that of [11,12,26,27]. Time is discrete and denoted by t = 0, .…”

Section: A Stochastic Model Of the Evolution Of The Network And Public Good Provisionmentioning

confidence: 99%

“…Additionally, Ref. [27] studied the co-evolution of networks and behavior in a potential game and showed how the model could be estimated. The contribution of this paper with respect to this literature is twofold.…”

Section: Introductionmentioning

confidence: 99%

“…The contribution of this paper with respect to this literature is twofold. First, contrarily to [12,27], we studied a game with non-linear best-replies. As a result, we could not make use of the techniques developed by [25] to characterize the equilibrium set.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Evolution of Networks and Local Public Good Provision: A Potential Approach

Kinateder

Merlino

2021

Games

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In this paper, we propose a game in which each player decides with whom to establish a costly connection and how much local public good is provided when benefits are shared among neighbors. We show that, when agents are homogeneous, Nash equilibrium networks are nested split graphs. Additionally, we show that the game is a potential game, even when we introduce heterogeneity along several dimensions. Using this result, we introduce stochastic best reply dynamics and show that this admits a unique and stationary steady state distribution expressed in terms of the potential function of the game. Hence, even if the set of Nash equilibria is potentially very large, the long run predictions are sharp.

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Section: The Steady State Distributionmentioning

confidence: 86%

“…The co-evolution of networks and behavior is a stochastic best-response dynamic similar to that of [11,12,26,27]. Time is discrete and denoted by t = 0, .…”

Section: A Stochastic Model Of the Evolution Of The Network And Public Good Provisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Evolution of Networks and Local Public Good Provision: A Potential Approach

Kinateder

Merlino

2021

Games

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“…Solving for such an equilibrium is not feasible, thus the estimation is performed using the maximum of first‐ and second‐order approximations of the potential function. Hsieh, König, and Liu (2019) used a similar framework to Badev (2013) while considering a continuous action under the context of firm R&D collaboration network and R&D expenditure. The microfoundation of their model is based on the stochastic best response dynamics (Blume (1993)) and the equilibrium is also only guaranteed for potential games.…”

Section: Introductionmentioning

confidence: 99%

Specification and estimation of network formation and network interaction models with the exponential probability distribution

Hsieh¹,

Lee²

2020

Self Cite

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We model network formation and interactions under a unified framework by considering that individuals anticipate the effect of network structure on the utility of network interactions when choosing links. There are two advantages of this modeling approach: first, we can evaluate whether network interactions drive friendship formation or not. Second, we can control for the friendship selection bias on estimated interaction effects. We provide microfoundations of this statistical model based on the subgame perfect equilibrium of a two‐stage game and propose a Bayesian MCMC approach for estimating the model. We apply the model to study American high school students' friendship networks using the Add Health dataset. From two interaction variables, GPA and smoking frequency, we find that the utility of interactions in academic learning is important for friendship formation, whereas the utility of interactions in smoking is not. However, both GPA and smoking frequency are subject to significant peer effects.

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Social Interaction Methods

Hsieh¹,

Xu²,

Patacchini³

2020

Handbook of Labor, Human Resources and Population Economics

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This chapter is concerned with methods for analyzing social interaction effects. The attention is focused on how to estimate endogenous effects, where an individual's choice may depend on those of his/her contacts about the same activity. The analysis is guided by the data structure that is available to measure social interactions, an intuitive aspect that allows empirical researchers to understand whether and how they could study social interaction effects in their own data. First, the case where the information on social interaction patterns is limited to membership to a given group is considered, then the discussion moves to the case where the data contain information on specific relationships among pairs of individuals within each group, and the availability of data on the co-evolution of social structures and outcomes. This chapter also discusses some basic methods to deal with online social network data, and the novel literature estimating social interaction effects relying only on outcome data. For each data structure, the challenges and the main methods proposed in the literature to tackle them are reviewed.

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A Structural Model for the Coevolution of Networks and Behavior

Cited by 15 publications

References 65 publications

The Evolution of Networks and Local Public Good Provision: A Potential Approach

The Evolution of Networks and Local Public Good Provision: A Potential Approach

Specification and estimation of network formation and network interaction models with the exponential probability distribution

Social Interaction Methods

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