A simple model illustrating RA Fisher’s (1930) reconciliation of Darwinian selection with particulate genetic contributions.

According to the philosopher of science K. Popper “All life is problem solving”. Genetic algorithms aim to leverage Darwinian selection, a fundamental mechanism of biological evolution, so as to tackle various engineering challenges.
Flibs’NFarol is an Agent Based Model that embodies a genetic algorithm applied to the inherently ill-defined “El Farol Bar” problem. Within this context, a group of agents operates under bounded rationality conditions, giving rise to processes of self-organization involving, in the first place, efficiency in the exploitation of available resources. Over time, the attention of scholars has shifted to equity in resource distribution, as well. Nowadays, the problem is recognized as paradigmatic within studies of complex evolutionary systems.
Flibs’NFarol provides a platform to explore and evaluate factors influencing self-organized efficiency and fairness. The model represents agents as finite automata, known as “flibs,” and offers flexibility in modifying the number of internal flibs states, which directly affects their behaviour patterns and, ultimately, the diversity within populations and the complexity of the system.

This model illustrates the processes underlying the social construction of reality through an agent-based genetic algorithm. By simulating the interactions of agents within a structured environment, we have demonstrated how shared information and popularity contribute to the formation of emergent social structures with diverse cultures. The model illustrates how agents balance environmentally valid information with socially reliable information. It also highlights how social interaction leads to the formation of stable, yet diverse, social groups.

A minimal genetic algorithm was previously developed in order to solve an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon seems relevant in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
For a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward to an environmental challenge.
The model aims to shed light on these controversial points of view and it provides also the features required to check the role of sex and genetic recombination in the mutator genes diffusion.

This abstract model explores the emergence of altruistic behavior in networked societies. The model allows users to experiment with a number of population-level parameters to better understand what conditions contribute to the emergence of altruism.

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

This project combines game theory and genetic algorithms in a simulation model for evolutionary learning and strategic behavior. It is often observed in the real world that strategic scenarios change over time, and deciding agents need to adapt to new information and environmental structures. Yet, game theory models often focus on static games, even for dynamic and temporal analyses. This simulation model introduces a heuristic procedure that enables these changes in strategic scenarios with Genetic Algorithms. Using normalized 2x2 strategic-form games as input, computational agents can interact and make decisions using three pre-defined decision rules: Nash Equilibrium, Hurwicz Rule, and Random. The games then are allowed to change over time as a function of the agent’s behavior through crossover and mutation. As a result, strategic behavior can be modeled in several simulated scenarios, and their impacts and outcomes can be analyzed, potentially transforming conflictual situations into harmony.

This is an original model of (sub)culture diffusion.
It features a set of agents (dubbed “partygoers”) organized initially in clusters, having properties such as age and a chromosome of opinions about 6 different topics. The partygoers interact with a set of cultures (also having a set of opinions subsuming those of its members), in the sense of refractory or unhappy members of each setting about to find a new culture and trading information encoded in the genetic string (originally encoded as -1, 0, and 1, resp. a negative, neutral, and positive opinion about each of the 6 traits/aspects, e.g. the use of recreational drugs). There are 5 subcultures that both influence (through the aforementioned genetic operations of mutation and recombination of chromosomes simulating exchange of opinions) and are influenced by its members (since a group is a weighted average of the opinions and actions of its constituents). The objective of this feedback loop is to investigate under which conditions certain subculture sizes emerge, but the model is open to many other kinds of explorations as well.

This NetLogo model simulates trait-based biotic responses to climate change in an environmentally heterogeneous continent in an evolving clade, the species of which are each represented by local populations that disperse and interbreed; they also are subject to selection, genetic drift, and local extirpation. We simulated mammalian herbivores, whose success depends on tooth crown height, vegetation type, precipitation and grit. This model investigates the role of dispersal, selection, extirpation, and other factors contribute to resilience under three climate change scenarios.

Large-scale land acquisitions (LSLAs) threaten smallholder livelihoods globally. Despite more than a decade of research on the LSLA phenomenon, it remains a challenge to identify governance conditions that may foster beneficial outcomes for both smallholders and investors. One potentially promising strategy toward this end is contract farming (CF), which more directly involves smallholder households in commodity production than conditions of acquisition and displacement.

To improve understanding of how CF may mediate the outcomes of LSLAs, we developed an agent-based model of smallholder livelihoods, which we used as a virtual laboratory to experiment on a range of hypothetical LSLA and CF implementation scenarios.

The model represents a community of smallholder households in a mixed crop-livestock system. Each agent farms their own land and manages a herd of livestock. Agents can also engage in off-farm employment, for which they earn a fixed wage and compete for a limited number of jobs. The principal model outputs include measures of household food security (representing access to a single, staple food crop) and agricultural production (of a single, staple food crop).

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