managing complexity with computational intelligence (macoi)
One of my main research lines nowadays is understanding how cooperation emerges in public good games (PGGs) and social dilemmas such as prisoners' dilemma (PD) or any other economic and social behavior.
Collective-risk dilemmas. This is a specific PGG model where there is a risk of failure. Source code for Chica and Santos 2022, Seeding leading cooperators and institutions in networked climate dilemmas
Fraud behavior for indirect taxes. We created a model, enriched with real data, to understand how companies initiate fraudulent VAT declaration.
Herding behavior and its relationship with cooperation.
Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. We investigate the emergent dynamics of an evolutionary game-theoretic model - the N-player evolutionary trust game - consisting of three types of players: an investor, a trustee who is trustworthy, and a trustee who is untrustworthy. Interactions between players are limited to local neighborhoods defined by a specific spatial topology or social network.
Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that it is possible to promote trust when players interact in a social network even if there is a substantial number of untrustworthy individuals in the initial population.
You can know more about this research line here
AGENT-BASED DSS FOR MARKETING
Many complex combinatorial and numerical optimization problems arise in human activities, such as economics (e.g., portfolio selection), industry (e.g., scheduling or logistics), or engineering (e.g., network routing), among many others.
An assembly line is a flow-oriented production system made up of a number of workstations, arranged in series and in parallel. Assembly lines are of great importance in the industrial production of high quantity standardized commodities and more recently even gained importance in low volume production of customized products. The assembly line configuration involves determining an optimal assignment of a subset of tasks to each station of the plant to minimize the inefficiency of the line or its total time while respecting all the constraints imposed on the tasks and on the stations such as the . Such problem is called assembly line balancing (ALB) and it is a very complex combinatorial optimization problem (known to be NP-hard) which has become an active field of research over more than half a century.
A more realistic extension of assembly line balancing is the TSALBP. This problem considers an additional space constraint to get a simplified but closer version to real-world problems, defining the Time and Space ALB problem (TSALBP). TSALBP presents eight variants depending on three optimization criteria: the line cycle time (c), the number of stations (m), and their area (A). Four of those variants present a multi-objective nature. We applied multiobjective metaheuristics (e.g., genetic algorithms, ACO, local search) as optimization methods to solve the problem.