Complexity
The TI-Complexity research field focuses on the analysis and empirical validation of behavioral micro, macro and finance models under “bounded” rationality, where agents are not fully rational, but behave in accordance with experimental and empirical evidence. A central question is: which emerging aggregate macro behavior arises through the interactions of individual micro decisions of boundedly rational heterogeneous agents? Focus topics are: Complex systems & nonlinear dynamics; Bounded rationality, learning & heterogeneous expectations; Behavioral & experimental macro-finance; Complex networks; Dynamic games in complex systems; Strategic interaction and (non-)cooperative games.
Complexity
The TI-Complexity research field focuses on the analysis and empirical validation of behavioral micro, macro and finance models under “bounded” rationality, where agents are not fully rational, but behave in accordance with experimental and empirical evidence. A central question is: which emerging aggregate macro behavior arises through the interactions of individual micro decisions of boundedly rational heterogeneous agents? Focus topics are: Complex systems & nonlinear dynamics; Bounded rationality, learning & heterogeneous expectations; Behavioral & experimental macro-finance; Complex networks; Dynamic games in complex systems; Strategic interaction and (non-)cooperative games.
Researchers in this field
Fellows
Fellows
PhD Students
PhD Students
Key Publications
Cornea-Madeira, A., Hommes, C. and Massaro, D. (2019). Behavioral Heterogeneity in U.S. Inflation Dynamics Journal of Business and Economic Statistics, 37(2):288--300.
Hommes, C. and Lustenhouwer, J. (2019). Inflation targeting and liquidity traps under endogenous credibility Journal of Monetary Economics, 107:48--62.
Arifovic, J., Hommes, C. and Salle, I. (2019). Learning to believe in simple equilibria in a complex OLG economy - evidence from the lab Journal of Economic Theory, 183:106--182.
Bao, T., Hommes, C. and Makarewicz, T. (2017). Bubble formation and (in)efficient markets in learning-to-forecast and optimise experiments Economic Journal, 127(605):F581--F609.
Diks, C. and Wolski, M. (2016). Nonlinear Granger Causality: Guidelines for Multivariate Analysis Journal of Applied Econometrics, 31(7):1333--1351.
Discussion Papers
20-054/II - Compromising between the proportional and equal division values: axiomatization, consistency and implementation
Date: September 01, 2020
20-043/II - Equal Loss under Separatorization and Egalitarian Values
Date: July 18, 2020
20-033/II - Individual weighted excess and least square values
Date: June 20, 2020
20-020/II - All symmetric equilibria in differential games with public goods
Date: April 20, 2020
20-019/II - On the Influence of Top Journals
Date: April 19, 2020