The goal of my research is to build market
models that are realistic and can describe actual behavior well. In my view standard rational models often
attach too much cognitive skills to economic agents while individuals or firms
are less sophisticated in reality. Therefore, it is important to understand how
real economic agents make decisions and how less sophisticated but reasonable
modes of behavior affect the market outcome. My research focuses on these two interrelated
issues.
Publications
 "Oligopoly Game: Price Makers Meet Price Takers" (2018), Journal of Economic Dynamics and Control, 91, pp. 84103
Joint work with Mikhail Anufriev
The paper studies an oligopoly game, where firms can choose between pricetaking and pricemaking strategies. On a mixed market price takers are always better off than price makers, though the profits of both types decline in the number of price takers. We investigate and confront two possibilities of firms' decisions about their types: forwardlooking equilibrium reasoning and backwardlooking individual learning. We find that the Cournot outcome is the only equilibrium prediction and it is learnable if firms are sufficiently sensitive to profit differences. However, with a larger number of firms, a unilateral deviation from Cournot behavior becomes profitable. Under learning this incentive creates a space for permanent oscillations over different markets with a positive but low number of price takers. Link to paper, Online Appendix  "The Coexistence of Stable Equilibria under Least Squares Learning" (2017), Journal of Economic Behavior & Organization, 141, pp. 277300
In this paper we consider firms that learn about market conditions by estimating the demand function using past market data. We show that learning may lead to suboptimal outcomes even when the estimated demand function perfectly fits the observations used in the regression and firms thus perceive to have learned the demand function correctly. We consider the Salop model with three firms and two types of consumers that differ in their sensitivity to product differences. Firms do not know the demand structure and they apply least squares learning to learn the demand function. In each period, firms estimate a linear perceived demand function and they play the perceived best response to the previousperiod price of the other firms. This learning rule can lead to three different outcomes: a selfsustaining equilibrium, the Nash equilibrium or an asymmetric learningequilibrium. In this last equilibrium one firm underestimates the demand for low prices and it attracts consumers with high sensitivity only. This type of equilibrium has not been found in the literature on least squares learning before. Both the Nash equilibrium and the asymmetric learningequilibrium are locally stable therefore the model has coexisting stable equilibria. Link to paper  "Learning Cycles in Bertrand Competition with Differentiated Commodities and Competing Learning Rules'' (2013), Journal of Economic Dynamics and Control, 37(12), pp. 25262581
Joint work with Mikhail Anufriev and Jan Tuinstra
This paper stresses the importance of heterogeneity in learning. We consider a Bertrand oligopoly with firms using either least squares learning or gradient learning for determining the price. We demonstrate that convergence properties of the rules are strongly affected by heterogeneity. In particular, gradient learning may become unstable as the number of gradient learners increases. Endogenous choice between the learning rules may induce cyclical switching. Stable gradient learning gives higher average profit than least squares learning, making firms switch to gradient learning. This can destabilize gradient learning which, because of decreasing profits, makes firms switch back to least squares learning. Link to paper
 "Heterogeneous Learning in Bertrand Competition with Differentiated Goods'' (2013) in A. Teglio, S. Alfarano, E. CamachoCuena and M. GinésVilar (Eds.) Managing Market Complexity: The Approach of Artificial Economics, Springer, pp. 155  166
This paper stresses that the coexistence of
different learning methods can have a substantial effect on the convergence
properties of these methods. We consider a Bertrand oligopoly with
differentiated goods in which firms either use least squares learning or
gradient learning for determining the price for a given period. These methods are wellestablished in oligopoly models but, up till now, are
used mainly in homogeneous setups. We illustrate that the stability of gradient
learning depends on the distribution of learning methods over firms: as the
number of gradient learners increases, the method may lose stability and become
less profitable. We introduce competition between the learning methods and show
that a cyclical switching between the methods may occur.
Link to book chapter
Working papers "Can successful forecasters help stabilize asset prices in a learning to forecast experiment?" (2018)
Joint work with Jean Paul Rabanal, Olga Rud and Jan Tuinstra
We conduct a Learning to Forecast asset pricing experiment where the market impact of individual forecasts evolves endogenously based on the forecasters' past accuracy. We investigate how endogenous impacts affect price stability and mispricing relative to the fundamental price. Our results suggest that endogenous impacts can destabilize markets when impacts are quite sensitive to forecast accuracy: Price dispersion increases compared to the baseline treatment where impacts are constant and independent of forecast accuracy. On the other hand, mispricing can be reduced when markets are relatively stable and impacts are moderately sensitive to forecast accuracy. Link to working paper
 "Endogenous Information Disclosure in Experimental Oligopolies" (2015), CeDEx Discussion Paper 1510, University of Nottingham
Joint work with Anita KopányiPeuker
We examine whether endogenously sharing firmspecific production levels yields a less competitive market, as voluntarily information sharing can show willingness to cooperate. In our laboratory experiment firms choose production levels as well as whether to share their past production levels with competitors. We find no difference in average total outputs across data aggregation (aggregate versus individual production) and information settings (two exogenous and one endogenous settings). However, subjects indeed use voluntary sharing to show their intentions to cooperate. If they share information, they produce significantly less than if they do not share information; resulting in more collusion with individual data. Link to working paper
 "PriceQuantity Competition under Strategic Uncertainty" (2013), CeNDEF working paper 1313, University of Amsterdam
We consider the market for a homogeneous good in which two firms simultaneously decide on both the price and the production level. Firms have meanvariance preferences and they hold probabilistic conjectures about the actions of the other firm. We numerically show that a purestrategy equilibrium may exist in this setup, unlike in the standard version of simultaneous pricequantity competition. We calculate the symmetric purestrategy equilibrium numerically and we analyze how it depends on the degree of risk aversion and the amount of uncertainty in the conjectures. We find that the more risk averse the firms are, the less they produce and the higher price they ask in equilibrium. Aggregate production exceeds market demand for low degrees of risk aversion but as firms become more risk averse, they will not serve the whole market in equilibrium. Our results show that firms react differently to price uncertainty than to output uncertainty. When price uncertainty increases, firms charge a higher price and they produce less. In contrast, higher output uncertainty leads to a lower price whereas production levels may increase as well as decrease. Link to working paper Work in progress 
