Learning in “Do-It-Yourself Lottery” with Full Information: A Computational Study
We study the kind of dynamics players using a belief-based learning model lead to in Barrow’s “do-it-yourself lottery” by an agent-based computational economics approach. The lottery is that players choose a positive integer that is expected to be the smallest, but no one else chooses. For this purpose, we consider a simple game form in which every player knows all players’ submissions at the time of their decision making. We use computer simulations to change the game setup and the parameters of the learning model. Our main findings are twofold: First, the distributions of the submitted and winning integers are different from those in equilibrium in many cases. Second, the game patterns are contingent upon the two parameters in the learning model and the game setup itself: While a lower-sensitivity parameter value often leads to a somewhat randomized behavior, in the case of a higher-sensitivity parameter, the collective behavior is either a single pattern or plural ones.
KeywordsAgent-based Computational economics Behavioral game Mixed-strategyequilibrium Belief-based learning
This chapter was modified and extended from the earlier version presented at the 7th International Workshop on Agent-based Approaches in Economic and Social Complex Systems (AESCS 2012) on January 17–19, 2012. We appreciate the participants, two anonymous referees, and editor-in-chief Prof. Tadahiko Murata for helpful comments and suggestions. Financial support from the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Young Scientists (B) (24710163) (Yamada) and by JSPS and ANR under the Joint Research Project, Japan–France CHORUS Program, “Behavioral and cognitive foundations for agent-based models (becoa)” (Terano), is gratefully acknowledged.
- 1.Barrow JD (2008) 100 essential things you didn’t know you didn’t know. Bodley Head, LondonGoogle Scholar
- 4.Brenner T (2006) Agent learning representation: advice on modelling economic learning. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics: agent-based computational economics, North-Holland, Amsterdam, vol 2. pp 895–947Google Scholar
- 9.Duffy J (2006) Agent-based models and human subject experiments. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics: agent-based computational economics, North-Holland, Amsterdam, vol 2. pp. 949–1012Google Scholar
- 12.Ho T-H, Camerer CF, Weigelt K (1998) Iterated dominance and iterated best response in experimental “p-beauty contests.” Am Econ Rev 88:947–969Google Scholar
- 15.Judd KL (2006) Computationally intensive analyses in economics. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics: agent-based computational economics, North-Holland, Amsterdam, vol 2. pp 881–893Google Scholar
- 16.Nagel R (1995) Unraveling in guessing games: an experimental study. Am Econ Rev 85:1313–1326Google Scholar
- 20.Vriend NJ (2006) ACE models of endogenous interactions. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics: agent-based computational economics, North-Holland, Amsterdam, vol 2. pp 1047–1079Google Scholar
- 22.Yamada T, Terano T (2010) Barrow’s “do-it-yourself lottery” in agent-based computational economics: a simple study. In: 16th International Conference on Computing in Economics and Finance (CEF 2010) 59Google Scholar
- 23.Yamada T, Terano T (2010) A simple strategy experiment in Barrow’s “do-it-yourself lottery.” In: 3rd World Congress on Social Simulation (WCSS 2010) CD-ROMGoogle Scholar
- 24.Yamada T, Terano T (2011) Learning in “do-it-yourself lottery” with aggregate information: a computational study. In: 7th International Conference of the European Social Simulation Association (ESSA 2011) CD-ROMGoogle Scholar