Experience-weighted Attraction Learning Model Research Articles

The Strategy Evolution in Double Auction Based on the Experience-Weighted Attraction Learning Model

The double auction is a widely applicable trading mechanism used to converge to competitive equilibrium in different markets from which multiple equilibriums and incomplete information may arise. Therefore, different learning models have been applied to facilitate bidding strategies for buyers and sellers in the market. However, due to the existence of problems in double auction markets such as individual bounded rationality and information incompleteness, it is still necessary to explore a more general learning model to depict the learning mechanism in double auction markets and predict the evolution processes of bidding strategies for both sides. Therefore, this paper aims at introducing the use of the experience-weighted attraction (EWA) model for double auction because it combines reinforcement learning with belief learning that then converts EWA in a suitable and interesting learning model for describing and improving individuals’ learning behavior. It can become an effective learning model for bidding strategies in the double auction. In addition to the use of the EWA for strategy evolution in the double auction, the impact of its different bidding strategy performance parameters will be also analyzed and compared with other learning models.

Learning Dynamics and Norm Psychology Supports Human Cooperation in a Large-Scale Prisoner’s Dilemma on Networks

In this work, we explore the role of learning dynamics and social norms in human cooperation on networks. We study the model recently introduced in [Physical Review E, 97, 042321 (2018)] that integrates the well-studied Experience Weighted Attraction learning model with some features characterizing human norm psychology, namely the set of cognitive abilities humans have evolved to deal with social norms. We provide further evidence that this extended model—that we refer to as Experience Weighted Attraction with Norm Psychology—closely reproduces cooperative patterns of behavior observed in large-scale experiments with humans. In particular, we provide additional support for the finding that, when deciding to cooperate, humans balance between the choice that returns higher payoffs with the choice in agreement with social norms. In our experiment, agents play a prisoner’s dilemma game on various network structures: (i) a static lattice where agents have a fixed position; (ii) a regular random network where agents have a fixed position; and (iii) a dynamic lattice where agents are randomly re-positioned at each game iteration. Our results show that the network structure does not affect the dynamics of cooperation, which corroborates results of prior laboratory experiments. However, the network structure does seem to affect how individuals balance between their self-interested and normative choices.

Heterogeneity in generalized reinforcement learning and its relation to cognitive ability

In this paper, we study the connections between working memory capacity (WMC) and learning in the context of economic guessing games. We apply a generalized version of reinforcement learning, popularly known as the experience-weighted attraction (EWA) learning model, which has a connection to specific cognitive constructs, such as memory decay, the depreciation of past experience, counterfactual thinking, and choice intensity. Through the estimates of the model, we examine behavioral differences among individuals due to different levels of WMC. In accordance with ‘Miller’s magic number’, which is the constraint of working memory capacity, we consider two different sizes (granularities) of strategy space: one is larger (finer) and one is smaller (coarser). We find that constraining the EWA models by using levels (granules) within the limits of working memory allows for a better characterization of the data based on individual differences in WMC. Using this level-reinforcement version of EWA learning, also referred to as the EWA rule learning model, we find that working memory capacity can significantly affect learning behavior. Our likelihood ratio test rejects the null that subjects with high WMC and subjects with low WMC follow the same EWA learning model. In addition, the parameter corresponding to ‘counterfactual thinking ability’ is found to be reduced when working memory capacity is low.

Dynamic Nonlinear Pricing Model Based on Adaptive and Sophisticated Learning

Existing dynamic pricing models which take consumers’ learning behavior into account generally assume that consumers learn on the basis of reinforcement learning and belief-based learning. Nevertheless, abundant empirical evidence of behavior game indicates that consumers’ learning is normally described as a process of mixed learning. Particularly, for experience goods, a consumer’s purchase decision is not only based on his previous purchase behavior (adaptive learning), but also affected by that of other consumers (sophisticated learning). With the assumption that consumers are both adaptive and sophisticated learners, we study a dynamic pricing model dealing with repeated decision problems in a duopoly market. Specifically, we build a dynamic game model based on sophisticated experience-weighted attraction learning model (SEWA) and analyze the existence of the equilibrium. Finally, we show the characteristics and differences of the steady-state solutions between models considering adaptive consumers and models considering sophistical consumers by numerical results.

Contract Complexity and Performance Under Asymmetric Demand Information: An Experimental Evaluation

Exploring the tension between theory and practice regarding complexity and performance in contract design is especially relevant. The goal of this paper is to understand why simpler contracts may commonly be preferred in practice despite being theoretically suboptimal. We study a two-tier supply chain with a single supplier and a single buyer to characterize the impact of contract complexity and asymmetric information on performance and to compare theoretical predictions to actual behavior in human subject experiments. In the experiments, the computerized buyer faces a newsvendor setting and has better information on end-consumer demand than the human supplier. The supplier offers either a quantity discount contract (with two or three price blocks) or a price-only contract, contracts that are commonplace in practice, yet different in complexity. Results show that, contrary to theoretical predictions, quantity discounts do not necessarily increase the supplier's profits. We also observe a more equitable distribution of profits between the supplier and the buyer than what theory predicts. These observations can be described with three decision biases (the probabilistic choice bias, the reinforcement bias, and the memory bias) and can be modeled using the experience-weighted attraction learning model. Our results demonstrate that simpler contracts, such as a price-only contract or a quantity discount contract with a low number of price blocks, are sufficient for a supplier designing contracts under asymmetric demand information.This paper was accepted by Christian Terwiesch, operations and supply chain management.

A Modeling of Game Learning Theory Based on Fairness

By incorporating fairness factor in the EWA (experience-weighted attraction) learning model, we develop an extended game learning model called FGL model. We use psychological effect in stead of material effect to modify strategy’s payoff and attraction, and to study the equilibrium movement further in dynamic Games. That participants have fair thinking will, in turn, lead to their psychological function changes. Compared with EWA learning model by simulating the decision-making in Ultimatum Game, we find FGL model converges to equilibrium strategy faster.

Efficiency Gains from Team-Based Coordination—Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision making so far. In reality, however, teams often have to coordinate with other teams. We present a series of coordination experiments with a total of 1,101 participants. We find that teams of three subjects each coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model. (JEL C71, C91, D23, D83, M54)

Efficiency Gains from Team-Based Coordination - Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision-making so far. In reality, however, teams often have to coordinate with other teams. We present an experiment with 825 participants, using six different coordination games, where either individuals or teams interact with each other. We find that teams coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model.

Efficiency Gains from Team-Based Coordination: Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision-making so far. In reality, however, teams often have to coordinate with other teams. We present an experiment with 825 participants, using six different coordination games, where either individuals or teams interact with each other. We find that teams coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model.

Learning under limited information

We study how human subjects learn under extremely limited information. We use Chen's (forthcoming) data on cost sharing games, and Van Huyck et al.'s (1996, Manuscript) data on coordination games to compare three payoff-based learning models. Under the serial mechanism and coordination games, the payoff-assessment learning model (Sarin and Vahid, 1999, Games Econ. Behav. 28, 294–309) tracks the data the best, followed by the experience-weighted attraction learning model (Camerer and Ho, 1999, Econometrica, 67, 827–874), which in turn, is followed by a simple reinforcement learning model. Under the average cost pricing mechanism, however, none of the learning models tracks the data well.

David vs. Goliath: An Analysis of Asymmetric Mixed-Strategy Games and Experimental Evidence

Mixed strategies are widely used to model strategic situations in diverse fields such as economics, marketing, political science, and biology. However, some of the implications of asymmetric mixed-strategy solutions are counterintuitive. We develop a stylized model of patent race to examine some of these implications. In our model two firms compete to develop a product and obtain a patent. However, one firm values the patent more because of its market advantages, such as brand reputation and distribution network. Contrary to some intuition, we find that the firm that values the patent less is likely to invest more aggressively in developing the product and will also win the patent more often. We argue that the reason for these counterintuitive results is inherent in the very concept of mixed strategy solution. In a laboratory test, we examine whether subjects' behavior conforms to the equilibrium predictions. We find that the aggregate behavior of our subjects is consistent with the game-theoretic predictions. With the help of the experience-weighted attraction (EWA) learning model proposed by Camerer and Ho (1999), we show that adaptive learning can account for the investment behavior of our subjects.We find that the EWA learning model tracks the investment decisions of our subjects well, whether we hold out trials or an entire group of subjects.

Experience-Weighted Attraction Learning in Coordination Games: Probability Rules, Heterogeneity, and Time-Variation

In earlier research we proposed an “experience-weighted attraction (EWA) learning” model for predicting dynamic behavior in economic experiments on multiperson noncooperative normal-form games. We showed that EWA learning model fits significantly better than existing learning models (choice reinforcement and belief-based models) in several different classes of games. The econometric estimation in that research adopted a representative agent approach and assumed that learning parameters are stationary across periods of an experiment. In addition, we used the logit (exponential) probability response function to transform attraction of strategies into choice probability. This paper allows for nonstationary learning parameters, permits two “segments” of players with different parameter values in order to allow for some heterogeneity, and compares the power and logit probability response functions. These specifications are estimated using experimental data from weak-link and median-action coordination games. Results show that players are heterogeneous and that they adjust their learning parameters over time very slightly. Logit probability response functions never fit worse than power functions, and generally fit better.