FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2104876007> ?p ?o ?g. }

• W2104876007 abstract "This thesis consists of three papers that study the relationships between information, competition, and cooperation in two novel environments. We first examine the competitive behavior of firms with private information in two-sided matching markets. This part of the thesis employs purely game-theoretic tools. Second, we study voluntary contributions towards a linear public good by players who are connected through a network. In this environment, we use experimental and theoretical techniques to examine the effects of different information treatments and network structures on contributions. In Chapter 2, we study the behavior of firms in a competitive market for workers. In particular, we study a game in which firms with private information compete for workers by committing to a single salary offer. Workers care only about salary and the matching process follows the deferred-acceptance approach introduced by Gale and Shapley (1962). For a two-firm, two-worker model, there exists a Bayesian-Nash equilibrium in which each firm type chooses a distributional strategy with interval support in the salary space. This equilibrium exhibits a separation of types, in the sense that types with a common most preferred worker choose non overlapping, adjacent supports. The type that makes higher offers is determined by the relative marginal value for the preferred worker. In larger markets, which replicate the two-firm, two-worker case, a comparable Bayesian-Nash equilibrium exists and the separation result endures. In the limit, there is no aggregate uncertainty about the realization of firm types, and competition is confined to the most popular worker type. Numerical results suggest that the finite market equilibrium strategies converge with replication to the corresponding equilibrium strategies in the limit case. Chapter 3 studies individual contributions in a repeated public goods experiment. Subjects are connected through a circle network, and consumption of the public good depends on a player's own contribution and the contributions of his neighbors. We study whether contributions depend on the nature of the information players are shown about others between rounds of the repeated game. We extend the approach of Arifovic and Ledyard (2009), which merges a modified model of other-regarding preferences (ORP) with a theory of learning. Our model predicts individual behavior that ranges from free-riding, to conditional cooperation, to unconditional giving. Many subjects switch between these different behavioral strategies across games with different information treatments. Individual contributions are remarkably consistent with our model, which combines other-regarding preferences, learning, and the information treatment. Both the data and model simulations suggest that learning (to play the benchmark Nash equilibrium of the game) is differential and contagious across players. Free-riders and unconditional givers learn faster than conditional cooperators, and provide an anchor that accelerates learning by their neighbors. These results suggest that the network or neighborhood structure may be important for contributions through its effects on learning. In Chapter 4, we extend the analysis of learning and contributions in network public goods experiments. Using a set of five different network structures, we examine three key aspects of individual behavior. First, we report a negative finding regarding the predictions from our theory of other-regarding preferences. The theory provides certain predictions about how a particular subject should and should not behave across networks. We find several violations of these predictions, particularly in small, complete network groups, but also in the larger, more interesting networks. Second, we report on the average contributions by players in groups that consist of all conditional cooperators. In the one-shot game, these groups have a continuum of equilibria, in which every player contributes the same amount. While one might expect contributions to average half of the endowment, we find in both the data and learning simulations that average contributions decline over time to less than half of the endowment. We conjecture that learning dynamics may provide a method of equilibrium selection, for players trying to coordinate on one equilibrium in the repeated game. Our main finding in this chapter is that learning is contagious in networks other than the circle, which we studied in Chapter 3. We find considerable evidence at the individual match level that free-riders and altruists provide an anchor that stabilizes behavior and accelerates learning by their conditional cooperator neighbors. Our analysis highlights the possibility that, even when the distribution of free-riders, altruists, and conditional cooperators is the same across networks, the different neighborhood structures may affect contributions differently through their effects on learning. Thus, the main contribution of this chapter is the confirmation that learning is contagious across a range of different network structures." @default.
• W2104876007 created "2016-06-24" @default.
• W2104876007 creator A5091026019 @default.
• W2104876007 date "2013-01-01" @default.
• W2104876007 modified "2023-09-26" @default.
• W2104876007 title "Essays on Information, Competition, and Cooperation" @default.
• W2104876007 cites W1574552323 @default.
• W2104876007 cites W1599656298 @default.
• W2104876007 cites W1974216640 @default.
• W2104876007 cites W1989192412 @default.
• W2104876007 cites W1990666582 @default.
• W2104876007 cites W2002430844 @default.
• W2104876007 cites W2003089824 @default.
• W2104876007 cites W2033061110 @default.
• W2104876007 cites W2034033745 @default.
• W2104876007 cites W2048201329 @default.
• W2104876007 cites W2068115726 @default.
• W2104876007 cites W2071667058 @default.
• W2104876007 cites W2093650407 @default.
• W2104876007 cites W2094208470 @default.
• W2104876007 cites W2096348520 @default.
• W2104876007 cites W2096375861 @default.
• W2104876007 cites W2099758346 @default.
• W2104876007 cites W2100136304 @default.
• W2104876007 cites W2114537400 @default.
• W2104876007 cites W2135410596 @default.
• W2104876007 cites W2143529993 @default.
• W2104876007 cites W2158486621 @default.
• W2104876007 cites W2160274729 @default.
• W2104876007 cites W2160665943 @default.
• W2104876007 cites W2163569945 @default.
• W2104876007 cites W2170451139 @default.
• W2104876007 cites W2621456012 @default.
• W2104876007 cites W3022808291 @default.
• W2104876007 cites W3121616661 @default.
• W2104876007 cites W3122520279 @default.
• W2104876007 cites W3123807625 @default.
• W2104876007 cites W3125314754 @default.
• W2104876007 cites W3125789561 @default.
• W2104876007 cites W3126056200 @default.
• W2104876007 doi "https://doi.org/10.7907/cqmd-pq41." @default.
• W2104876007 hasPublicationYear "2013" @default.
• W2104876007 type Work @default.
• W2104876007 sameAs 2104876007 @default.
• W2104876007 citedByCount "0" @default.
• W2104876007 crossrefType "dissertation" @default.
• W2104876007 hasAuthorship W2104876007A5091026019 @default.
• W2104876007 hasConcept C105795698 @default.
• W2104876007 hasConcept C162324750 @default.
• W2104876007 hasConcept C165064840 @default.
• W2104876007 hasConcept C175444787 @default.
• W2104876007 hasConcept C177142836 @default.
• W2104876007 hasConcept C18903297 @default.
• W2104876007 hasConcept C200594392 @default.
• W2104876007 hasConcept C2780090960 @default.
• W2104876007 hasConcept C33923547 @default.
• W2104876007 hasConcept C34447519 @default.
• W2104876007 hasConcept C38652104 @default.
• W2104876007 hasConcept C41008148 @default.
• W2104876007 hasConcept C46814582 @default.
• W2104876007 hasConcept C73795354 @default.
• W2104876007 hasConcept C86803240 @default.
• W2104876007 hasConcept C91306197 @default.
• W2104876007 hasConcept C99221444 @default.
• W2104876007 hasConceptScore W2104876007C105795698 @default.
• W2104876007 hasConceptScore W2104876007C162324750 @default.
• W2104876007 hasConceptScore W2104876007C165064840 @default.
• W2104876007 hasConceptScore W2104876007C175444787 @default.
• W2104876007 hasConceptScore W2104876007C177142836 @default.
• W2104876007 hasConceptScore W2104876007C18903297 @default.
• W2104876007 hasConceptScore W2104876007C200594392 @default.
• W2104876007 hasConceptScore W2104876007C2780090960 @default.
• W2104876007 hasConceptScore W2104876007C33923547 @default.
• W2104876007 hasConceptScore W2104876007C34447519 @default.
• W2104876007 hasConceptScore W2104876007C38652104 @default.
• W2104876007 hasConceptScore W2104876007C41008148 @default.
• W2104876007 hasConceptScore W2104876007C46814582 @default.
• W2104876007 hasConceptScore W2104876007C73795354 @default.
• W2104876007 hasConceptScore W2104876007C86803240 @default.
• W2104876007 hasConceptScore W2104876007C91306197 @default.
• W2104876007 hasConceptScore W2104876007C99221444 @default.
• W2104876007 hasLocation W21048760071 @default.
• W2104876007 hasOpenAccess W2104876007 @default.
• W2104876007 hasPrimaryLocation W21048760071 @default.
• W2104876007 hasRelatedWork W101245830 @default.
• W2104876007 hasRelatedWork W1493336649 @default.
• W2104876007 hasRelatedWork W1556980428 @default.
• W2104876007 hasRelatedWork W1562291009 @default.
• W2104876007 hasRelatedWork W2022037572 @default.
• W2104876007 hasRelatedWork W2024575569 @default.
• W2104876007 hasRelatedWork W2057393778 @default.
• W2104876007 hasRelatedWork W2080248761 @default.
• W2104876007 hasRelatedWork W2139465705 @default.
• W2104876007 hasRelatedWork W2165798179 @default.
• W2104876007 hasRelatedWork W2528723413 @default.
• W2104876007 hasRelatedWork W2796812035 @default.
• W2104876007 hasRelatedWork W2884133493 @default.
• W2104876007 hasRelatedWork W3033139707 @default.
• W2104876007 hasRelatedWork W3042525782 @default.
• W2104876007 hasRelatedWork W3122905424 @default.

• next