A continuous model of strong and weak ties

Griffith, Alan

doi:10.1111/jpet.12611

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…A first step could be to differentiate between two types of agents, where linking to one of the types is more profitable than linking to the other. Griffith (2020) has started to analyze equilibrium networks in the presence of agent heterogeneity for the case that link utility is given by

v false(t_{i j}, t_{j i} false) = t_{i j}^{α} t_{j i}^{β}

with

α, β > 0

. Heterogeneity is introduced by scaling link utilities with agent‐specific weights and by allowing individuals to have different costs of self‐investment.…”

Section: Concluding Commentsmentioning

confidence: 99%

A model of weighted network formation

Baumann¹

2021

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This paper proposes a game of weighted network formation in which each agent has a limited resource to form links of possibly different intensities with other agents and to use for private purposes. We show that every equilibrium is either “reciprocal” or “nonreciprocal.” In a reciprocal equilibrium, any two agents invest equally in the link between them. In a nonreciprocal equilibrium, agents are partitioned into “concentrated” and “diversified” agents, and a concentrated agent is only linked to diversified agents and vice versa. For every link, the concentrated agent invests more in the link than the diversified agent. The unweighted relationship graph of an equilibrium, in which two agents are linked if they both invest positively in each other, uniquely predicts the equilibrium values of each agent's network investment and utility level, as well as the ratio of any two agents' investments in each other. We show that equilibria are not pairwise stable and are not efficient due to the positive externalities of investing in a link.

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v false(t_{i j}, t_{j i} false) = t_{i j}^{α} t_{j i}^{β}

with

α, β > 0

. Heterogeneity is introduced by scaling link utilities with agent‐specific weights and by allowing individuals to have different costs of self‐investment.…”

Section: Concluding Commentsmentioning

confidence: 99%

A model of weighted network formation

Baumann¹

2021

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“Friends Are Thieves of Time”: Heuristic attention sharing in stable friendship networks

Tenev

2024

Journal of Economic Behavior & Organization

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Random Assignment with Nonrandom Peers: A Structural Approach to Counterfactual Treatment Assessment

Griffith

2024

Review of Economics and Statistics

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Efforts to leverage peer effects by changing assignment have often fallen short due to endogenous peer choice. To address this, I build a two-part model: agents form networks via continuous linking decisions; conditional on realized networks, outcomes are determined. I provide results on identification of both parts of the model. Using data from a randomized study in India, I estimate the model, assess its performance in out-of-sample prediction, and simulate outcomes under preferential assignment rules. This paper contributes new methodology for identifying effects of alternative assignments in the presence of network endogeneity, as well as identification of network formation models.

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A continuous model of strong and weak ties

Cited by 5 publications

References 34 publications

A model of weighted network formation

A model of weighted network formation

“Friends Are Thieves of Time”: Heuristic attention sharing in stable friendship networks

Random Assignment with Nonrandom Peers: A Structural Approach to Counterfactual Treatment Assessment

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