Confusion and Learning in the Voluntary Contributions Game

Confusion and Learning in the Voluntary Contributions Game
Date:     2013-01
By:     Spiros Bougheas (School of Economics, University of Nottingham)
Jeroen Nieboer (School of Economics, University of Nottingham)
Martin Sefton (School of Economics, University of Nottingham)

We investigate experimentally the effect of consultation (unincentivized advice) on choices under risk in an incentivized investment task. We compare these choices to two benchmark treatments: one with isolated individual choices, and a second with group choice after communication. Our benchmarking treatments replicate earlier findings that groups take more risk than individuals in the investment task . In our consultation treatments we find evidence of peer effects: there is significant correlation of decisions within the peer group. However, average risk taking is not significantly different from the benchmark treatment with isolated individual choices. This latter result underlines the importance of payoff-commonality for bringing about higher risk-taking in groups.

Keywords:     experimental economics, choice under risk, advice, social influence, peer effects

==notes by yinung=

此篇和已發表在 Experimental Economics 題目竟然完全相同

Bayer, Ralph-C., Elke Renner, and Rupert Sausgruber. “Confusion and learning in the voluntary contributions game." Experimental Economics (2013): 1-19. [link to EE]


We use a limited information environment to assess the role of confusion in the repeated voluntary contributions game. A comparison with play in a standard version of the game suggests, that the common claim that decision errors due to confused subjects biases estimates of cooperation upwards, is not necessarily correct. Furthermore, we find that simple learning cannot generate the kind of contribution dynamics commonly attributed to the existence of conditional cooperators. We conclude that cooperative behavior and its decay observed in public goods games is not a pure artefact of confusion and learning.