Matching with Couples: a Multidisciplinary Survey

This survey deals with two-sided matching markets where one set of agents (workers/residents) has to be matched with another set of agents (firms/hospitals). We first give a short overview of a selection of classical results. Then, we review recent contributions to a complex and representative case of matching with complementarities, namely matching markets with couples. We discuss contributions from computer scientists, economists, and game theorists.matching; couples; stability; computational complexity; incentive compatibility; restricted domains; large markets

Mixture and distribution of different water qualities: an experiment on vertical structure in a complex market

We report results from experimental markets in which two different...water are supplied to two types of consumers; households and farmers. In the...studied, we very strategic complexity (and centralization) by varying the...of agents per market. Centralization of information by a multiproduct more (scenario I) improves market preformance with respect to a duopoly...downstream coordinator (scenario 3)succeds in mitigating upstream market...In a complex setup like ours, some centralization on the supply or the de...may enhance market efficiency.Publicad

Complex evolutionary systems in behavioral finance

Traditional finance is built on the rationality paradigm. This chapter discusses simple models from an alternative approach in which financial markets are viewed as complex evolutionary systems. Agents are boundedly rational and base their investment decisions upon market forecasting heuristics. Prices and beliefs about future prices co-evolve over time with mutual feedback. Strategy choice is driven by evolutionary selection, so that agents tend to adopt strategies that were successful in the past. Calibration of "simple complexity models" with heterogeneous expectations to real financial market data and laboratory experiments with human subjects are also discussed.

Production, Trade, Prices, Exchange Rates and Equilibration in Large Experimental Economies

We study market equilibration in laboratory economies that are larger and more complex than any that have been studied experimentally to date. Complexity is derived from the fact that the economies are international in economic structure with multiple input, output, and foreign exchange markets in operation. The economies have twenty-one markets and due to the fact that they have roughly �fifty agents, the economies are characterized by several hundred equations. In spite of the complexity and interdependence of the economy, the results demonstrate the substantial power of the general equilibrium model of perfect competition to predict the direction of movement of market-level variables. Empirical patterns in the convergence process are explored and described

Mixture and distribution of different water qualities: an experiment on vertical structure in a complex market.

We report results from experimental markets in which two different...water are supplied to two types of consumers; households and farmers. In the...studied, we very strategic complexity (and centralization) by varying the...of agents per market. Centralization of information by a multiproduct more (scenario I) improves market preformance with respect to a duopoly...downstream coordinator (scenario 3)succeds in mitigating upstream market...In a complex setup like ours, some centralization on the supply or the de...may enhance market efficiency.water quality; experimental market; complex system;

Model Selection in an Information Economy : Choosing what to Learn

As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance. In contrast to traditional completeinformation economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period. By explicitly considering the both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.Online learning; information economics; model selection; direct search

Heterogeneity, Market Mechanisms, and Asset Price Dynamics

This chapter surveys the boundedly rational heterogeneous agent (BRHA) models of financial markets, to the development of which the authors and several co-authors have contributed in various papers. We give particular emphasis to role of the market clearing mechanism used, the utility function of the investors, the interaction of price and wealth dynamics, portfolio implications, the impact of stochastic elements on the markets dynamics, and calibration of this class of models. Due to agents’ behavioural features and market noise, the BRHA models are both nonlinear and stochastic. We show that the BRHA models produce both a locally stable fundamental equilibrium corresponding to that of standard paradigm, as well as instability with a consequent rich range of possible complex behaviours characterised both indirectly by simulation and directly by stochastic bifurcations. A calibrated model is able to reproduce quite well the stylized facts of financial markets. The BRHA framework is thus able to accommodate market features that seem not easily reconcilable for the standard financial market paradigm, such as fat tail, volatility clustering, large excursions from the fundamental and bubbles.Bounded rationality; interacting heterogeneous agents; behavioural finance; nonlinear economic dynamics; complexity

Modelling crypto markets by multi-agent reinforcement learning

Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period