The limit of targeting in networks

Jian Li, Junjie Zhou and Ying-Ju Chen

Journal of Economic Theory, 2022, vol. 201, issue C

Abstract: This paper studies the value of network-based targeting within a class of network games with strategic complements, where the designer can target a group of seed players as first movers. We define an effectiveness index, called the relative network synergy equivalent (RNSE), to quantify the effect of a network-based targeting intervention relative to across-the-board peer effect enhancement. We show that, regardless of the targeting policies and network structures, a universal and tight upper bound for this index is 2≈1.414. This upper bound is robust to considerations of revenue maximization, costly seeding, random seeding as the alternative benchmark, and multiple stages of moves. Compared with network-based targeting, peer effect enhancing policy has the advantage of being agnostic to the network structure. In the case of small synergy, we provide comparative statics of the RNSE index concerning the network structures: fixing the targeting policy, increasing network links within the seeded group or the unseeded group will decrease the index; meanwhile, adding links across these two groups will increase the index. Our analysis sheds light on policy choices between network-based targeting and peer effect enhancing policies.

Keywords: Network games; Targeting; Strategic complements; Synergy (search for similar items in EconPapers)
JEL-codes: D21 D29 D82 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:201:y:2022:i:c:s0022053122000084

DOI: 10.1016/j.jet.2022.105418

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