
In the ultimatum game, the challenge is to explain why responders reject non-zero offers thereby defying classical rationality. Fairness and related notions have been the main explanations so far. We explain this rejection behavior via the following principle: if the responder regrets less about losing the offer than the proposer regrets not offering the best option, the offer is rejected. This principle qualifies as a rational punishing behavior and it replaces the experimentally falsified classical rationality (the subgame perfect Nash equilibrium) that leads to accepting any non-zero offer. The principle is implemented via the transitive regret theory for probabilistic lotteries. The expected utility implementation is a limiting case of this. We show that several experimental results normally prescribed to fairness and intent-recognition can be given an alternative explanation via rational punishment; e.g. the comparison between "fair" and "superfair", the behavior under raising the stakes etc. Hence we also propose experiments that can distinguish these two scenarios (fairness versus regret-based punishment). They assume different utilities for the proposer and responder. We focus on the mini-ultimatum version of the game and also show how it can emerge from a more general setup of the game.
13 pages, 2 figures
FOS: Economics and business, FOS: Computer and information sciences, Physics - Physics and Society, Computer Science - Computer Science and Game Theory, Economics - Theoretical Economics, Theoretical Economics (econ.TH), FOS: Physical sciences, Physics and Society (physics.soc-ph), Computer Science and Game Theory (cs.GT)
FOS: Economics and business, FOS: Computer and information sciences, Physics - Physics and Society, Computer Science - Computer Science and Game Theory, Economics - Theoretical Economics, Theoretical Economics (econ.TH), FOS: Physical sciences, Physics and Society (physics.soc-ph), Computer Science and Game Theory (cs.GT)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
