Vikas Chaudhary

Vikas Chaudhary

Doctor of Philosophy

About

3
Publications
205
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Additional affiliations
October 2018 - December 2019
University College London
Position
  • Post Graduate Teaching Assistant
September 2015 - August 2021
University of Exeter
Position
  • PhD Student
July 2014 - June 2015
Indian Statistical Institute
Position
  • Research Assistant
Education
September 2015 - February 2022
University of Exeter
Field of study
  • Economics
July 2012 - May 2014
Indian Statistical Institute
Field of study
  • Economics

Publications

Publications (3)
Preprint
Full-text available
We study contests as an example of winner-take-all competition with linearly ordered large strategy space. We study a model in which each player optimizes the probability of winning above some subjective threshold. The environment we consider is that of limited information where agents play the game repeatedly and know their own efforts and outcome...
Preprint
Full-text available
Cooperation can be achieved via incentives from future interactions, specifically in the case of public monitoring. But, today, our social and professional spheres keep shifting rapidly and we interact often with strangers. We are interested in such sporadic interactions which can be modeled as a continuous Prisoner's Dilemma in an environment of t...
Preprint
Full-text available
Media reports say that high earners and syndicates buy lottery tickets in bulk. Experimental evidence shows that agents aggressively bid in auctions and contests. Do people try to trade-off probability of winning with other basic risk dimensions (for example, cost) to achieve a subjective threshold probability of winning (in environments they can)...

Projects

Projects (3)
Project
The behavioural regularities of increasing effort after losing, decreasing effort after winning, dropping out after consecutive losing and over-dissipation at the aggregate level are often found in experimental data in strategic games including the ones which can be modeled as contests and all pay auctions. The environment we consider is that of limited information where agents play the game repeatedly and know their own efforts and outcomes. We assume that in such an environment agents decision making is driven by some threshold probability of winning each period. We formulate a two parameter reinforcement learning model which has features of direction learning and can make a plausible explanation for such behavioural regularities together. The model can broadly explain the experimentally observed behaviour in Tullock contests in such an environment where other predominant theories are not able to.
Project
Media reports show that high earners and syndicates buy lottery tickets in bulk. Experimental evidence shows that agents aggressively bid in auctions and contests. Do people make trade-offs between cost/effort and the probability of winning (in environments they can) to reach a suitable chance of winning? The literature on risky choices suggests so. In the main design of this experiment, we deconstruct the expected value with variance and skewness of a lottery with Bernoulli distribution to examine the decision making process. Based on the results, a proportion is classified as expected utility maximizer (EUM) while another proportion is trading off cost with the probability of winning and apparently have a minimum probability of winning (MPW). More MPWs prefer higher probabilities compared to EUMs in a constant value lottery set which may explain preference for negative skewness in experiments. Additionally, we test two contests designs and find MPWs in the population which may explain the puzzle of equilibrium effort more than risk-neutral Nash equilibrium in experiments.
Project
Cooperation can be achieved via incentives from future interactions, specifically in case of public monitoring. But, today, our social and professional spheres keep shifting rapidly and we interact often with strangers. We are interested in such sporadic interactions which can be modeled as a continuous iterated Prisoner’s Dilemma in an environment of the symmetric market where the whole population is competing among themselves to interact with other agents who will contribute the most. The interaction is private, only the agents involved know how much have they contributed to each others’ well-being and partners may change next period. In such an environment if the reputation of agents is not available then there is no incentive to co-operate. In this paper, we show that if a experience reporting mechanism facilitates assortative matching then cooperation and honest reporting is evolutionarily (neutral) stable.