“…It is worth noting that convergence implies that regions of higher density stay fixed even after producing new generations. This result contrasts with the ones obtained in a previous work done by some of the authors of this paper [13] which we reproduce in figure 7a. In this case, a similar variation of the Yard-Sale model was studied, but the mechanism introduced to reduce inequality in the system consisted in adding a social protection factor f that favoured the probability of the poorest agent of winning each transaction.…”
Section: (A) Using Risk As the Inputcontrasting
confidence: 99%
“…We define an input vector vifalse(tfalse) containing all the information available to agent i at time t and use an evolutionary algorithm to find the optimal function rifalse(t+1false)=Rfalse(vifalse(tfalse)false) that delivers a new value of risk. In figure 6, we illustrate the algorithm used to drive the dynamics of a system of N agents, whose initial wealth and risk are uniformly distributed in the interval false[0,1false] (see [13] for more details). Figure 6Illustration of the genetic algorithm used to train the neural networks of the rational agents in the Yard–Sale Model.…”
Section: Rational Agentsmentioning
confidence: 99%
“…A new generation of agents is then created, where each agent will possess an imperfect copy of a neural network of the previous generation. Figure taken from [13]. …”
Section: Rational Agentsmentioning
confidence: 99%
“…They are not allowed to optimize their earnings. Here, instead, we combine the rational behaviour of agents discussed in [13] with taxes to diminish inequality.…”
Section: Introductionmentioning
confidence: 99%
“…Aiming to obtain distributions with power law tails, as described by Pareto, several methods have been proposed to introduce a multiplicative factor in the exchanges. Keeping the constraint of wealth conservation, a well-known proposition is that each agent saves a fraction—constant or random—of their resources [6–13]. With this approach in mind, each agent i is characterized by a wealth wi and a risk-aversion factor 0≤βi≤1.…”
Genetic machine learning (ML) algorithms to train agents in the Yard–Sale model proved very useful for finding optimal strategies that maximize their wealth. However, the main result indicates that the more significant the fraction of rational agents, the greater the inequality at the collective level. From social and economic viewpoints, this is an undesirable result since high inequality diminishes liquidity and trade. Besides, with very few exceptions, most agents end up with zero wealth, despite the inclusion of rational behaviour. To deal with this situation, here we include a taxation–redistribution mechanism in the ML algorithm. Previous results show that simple regulations can considerably reduce inequality if agents do not change their behaviour. However, when considering rational agents, different types of redistribution favour risk-averse agents, to some extent. Even so, we find that rational agents looking for optimal wealth can always arrive to an optimal risk, compatible with a particular choice of parameters, but increasing inequality.
This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.
“…It is worth noting that convergence implies that regions of higher density stay fixed even after producing new generations. This result contrasts with the ones obtained in a previous work done by some of the authors of this paper [13] which we reproduce in figure 7a. In this case, a similar variation of the Yard-Sale model was studied, but the mechanism introduced to reduce inequality in the system consisted in adding a social protection factor f that favoured the probability of the poorest agent of winning each transaction.…”
Section: (A) Using Risk As the Inputcontrasting
confidence: 99%
“…We define an input vector vifalse(tfalse) containing all the information available to agent i at time t and use an evolutionary algorithm to find the optimal function rifalse(t+1false)=Rfalse(vifalse(tfalse)false) that delivers a new value of risk. In figure 6, we illustrate the algorithm used to drive the dynamics of a system of N agents, whose initial wealth and risk are uniformly distributed in the interval false[0,1false] (see [13] for more details). Figure 6Illustration of the genetic algorithm used to train the neural networks of the rational agents in the Yard–Sale Model.…”
Section: Rational Agentsmentioning
confidence: 99%
“…A new generation of agents is then created, where each agent will possess an imperfect copy of a neural network of the previous generation. Figure taken from [13]. …”
Section: Rational Agentsmentioning
confidence: 99%
“…They are not allowed to optimize their earnings. Here, instead, we combine the rational behaviour of agents discussed in [13] with taxes to diminish inequality.…”
Section: Introductionmentioning
confidence: 99%
“…Aiming to obtain distributions with power law tails, as described by Pareto, several methods have been proposed to introduce a multiplicative factor in the exchanges. Keeping the constraint of wealth conservation, a well-known proposition is that each agent saves a fraction—constant or random—of their resources [6–13]. With this approach in mind, each agent i is characterized by a wealth wi and a risk-aversion factor 0≤βi≤1.…”
Genetic machine learning (ML) algorithms to train agents in the Yard–Sale model proved very useful for finding optimal strategies that maximize their wealth. However, the main result indicates that the more significant the fraction of rational agents, the greater the inequality at the collective level. From social and economic viewpoints, this is an undesirable result since high inequality diminishes liquidity and trade. Besides, with very few exceptions, most agents end up with zero wealth, despite the inclusion of rational behaviour. To deal with this situation, here we include a taxation–redistribution mechanism in the ML algorithm. Previous results show that simple regulations can considerably reduce inequality if agents do not change their behaviour. However, when considering rational agents, different types of redistribution favour risk-averse agents, to some extent. Even so, we find that rational agents looking for optimal wealth can always arrive to an optimal risk, compatible with a particular choice of parameters, but increasing inequality.
This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
