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Péter Kerényi

Publications and source records attributed to Péter Kerényi.

2 recordsLinked to original sources

Self-respecting worker in the precarious gig economy: A dynamic principal-agent model

We develop a continuous-time principal-agent model of gig work, where contractual flexibility allows the employer to adjust fixed pay and output-based incentives dynamically. The worker's participation depends on a backward-looking reference value--an exponentially weighted average of net payoff from past employment--capturing a self-respect--driven wage demand. If an offer's expected utility falls short of the reference value, the worker rejects the contract, resulting in temporary unemployment. Accepted contracts transmit output volatility to the worker through a sensitivity parameter, inducing instability into earnings and employment. The employer's optimal threshold policies strategically use this volatility transfer to regulate the worker's wage demands. In the first-best case, the principal imposes maximal volatility to drive the reference value down to its minimum, after which volatility transfer ceases entirely. In the second-best case, the need to incentivize effort ensures ongoing volatility transfer. Stationary analysis predicts a humped-shaped relationship between worker sensitivity and the principal's profit. If sensitivity is too low, the worker's wage demands are overly rigid and difficult to regulate, while if it is too high, their demands become excessively volatile, which eventually erodes the principal's control.

econ.GN↗

Estimation of the Shapley value by ergodic sampling

The idea of approximating the Shapley value of an n-person game by Monte Carlo simulation was first suggested by Mann and Shapley (1960) and they also introduced four different heuristical methods to reduce the estimation error. Since 1960, several statistical methods have been developed to reduce the standard deviation of the estimate. In this paper, we develop an algorithm that uses a pair of negatively correlated samples to reduce the variance of the estimate. Although the observations generated are not independent, the sample is ergodic (obeys the strong law of large numbers), hence the name "ergodic sampling". Unlike Shapley and Mann, we do not use heuristics, the algorithm uses a small sample to learn the best ergodic transformation for a given game. We illustrate the algorithm on eight games with different characteristics to test the performance and understand how the proposed algorithm works. The experiments show that this method has at least as low variance as an independent sample, and in five test games, it significantly improves the quality of the estimation, up to 75 percent.

cs.GT↗