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Title: Essays on microeconomic theory and behavioural economics
Author: Chen, Zhuoqiong
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2016
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Abstract:
The dissertation consists of four chapters. The first two chapters are devoted to exploring information acquisition and disclosure in contests. The third chapter is devoted to exploring how risk attitude affects bidding behaviour in all-pay auctions. the last chapter is denoted to exploring behavioural biases in advice-giving. In Chapter 1, I study player's incentive to spy on opponents' private information in contests. I show that each player's equilibrium effort is non-decreasing (non-increasing) in the posterior probability that the opponent has the same (a different) valuation. Accounting for the cost of spying, players are strictly better off than not spying on each other at all. In Chapter 2, I focus on how a contest organiser should disclose information in order to achieve certain objectives. In particular, I compare private signals with public signals. I show that there is no general ranking of the two signals in terms of the performance of maximizing players' expected payoff, but public signals outperforms private signals in maximizing expected effort. In Chapter 3 (co-authors with David Ong and Ella Segev), we extend previous theoretical work on n-players complete information all-pay auction to incorporate heterogeneous risk and loss averse utility functions. We provide sufficient and necessary conditions for the existence of equlibria with a given set of active players with any strictly increasing utility functions and characterize the players' equilibrium mixed strategies. Finally, in Chapter 4 (co-authored with Tobias Gesche), we show experimental evidence that a one-ff incentive to bias advice has a persistent effect on advisers' own actions and their future recommendations.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.702974  DOI: Not available
Keywords: HG Finance
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