Coordination through social learning in a general equilibrium model

Salle, Isabelle; Yıldızoğlu, Murat; Zumpe, Martin; Sénégas, Marc-Alexandre

doi:10.1016/j.jebo.2017.05.020

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…Naive evolutionists may believe that letting evolution establish "preferences" of individuals would conduce to a socially "optimal" state, as the golden rule consumption is certainly very close to our intuition about social optimality. 3 3 However, as an anonymous referee has pointed out, evolutionary forces can lead to free riding phenomena as Vriend (2000), Vallée and Yildizoglu (2009) and Salle et al (2017) have demonstrated. To understand why increasing selection pressure may cause harm to society we have to look at the fate of the different types.…”

Section: Main Fi Ndingsmentioning

confidence: 99%

Saver types: An evolutionary-adaptive approach

Varga

Vincze

2019

Soc Ec

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We set up an agent-based macro-model that focuses on the consumption-saving decision. Agents do not maximize utility, but their behaviour features certain "rational" aspects of human choice based on the idea of ecological rationality. Three qualitatively different saving strategies are defi ned: 1, buffer stock saving (prudent and forward looking); 2, permanent income saving (forward looking without prudence); and 3, myopic saving (caring only about immediate consumption). It is found that prudent saving behaviour becomes prevalent when the selection pressure is very high, but an economy comprising only prudent households tends to accumulate capital in excess of what is implied by the Golden Rule. Lowering the intensity of evolutionary forces results in more diversity in saver types, and, somewhat puzzlingly, may be socially benefi cial in some sense.

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Section: Main Fi Ndingsmentioning

confidence: 99%

Saver types: An evolutionary-adaptive approach

Varga

Vincze

2019

Soc Ec

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“…Studies have also examined cognitive traits and biases in individual financial decision making [14], yet few have revealed the global impact of individual cognitive traits and biases in large populations of economic agents on the quantifiable financial market dynamics [21]. Such approaches have also used methods from reinforcement learning [22][23][24] or adaptive learning [25,26]. The framework of reinforcement learning has multiple parallels with decision processes in the brain [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study

Lussange,

Vrizzi,

Palminteri

et al. 2024

PLoS ONE

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Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

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Coordination through social learning in a general equilibrium model

Cited by 2 publications

References 37 publications

Saver types: An evolutionary-adaptive approach

Saver types: An evolutionary-adaptive approach

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study

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