The model presented here was created as part of my dissertation. It aims to study the impacts of topography and climate change on prehistoric networks, with a focus on the Magdalenian, which is dated to between 20 and 14,000 years ago.

This model builds on the Armature distribution within the PaleoscapeABM model, which is itself a variant of the PaleoscapeABM available here written by Wren and Janssen, and.

This model aims to explore where and how much shellfish is discarded at coastal and non-coastal locations by daily coastal foraging. We use this model’s output to test the idea that we can confidently use the archaeological record to evaluate the importance of shellfish in prehistoric people’s diets.

The recognition that aquatic adaptations likely had significant impacts on human evolution triggered an explosion of research on that topic. Recognizing coastal foraging in the past relies on the archaeological signature of that behavior. We use this model to explore why some coastal sites are very intensely occupied and see if it is due to the shellfish productivity of the coast.

Knowledge Based Economy (KBE) is an artificial economy where firms placed in geographical space develop original knowledge, imitate one another and eventually recombine pieces of knowledge. In KBE, consumer value arises from the capability of certain pieces of knowledge to bridge between existing items (e.g., Steve Jobs illustrated the first smartphone explaining that you could make a call with it, but also listen to music and navigate the Internet). Since KBE includes a mechanism for the generation of value, it works without utility functions and does not need to model market exchanges.

To investigate the potential of using Social Psychology Theory in ABMs of natural resource use and show proof of concept, we present an exemplary agent-based modelling framework that explicitly represents multiple and hierarchical agent self-concepts

A friendship game is a kind of network game: a game theory model on a network. This is a NetLogo model of an agent-based adaptation of “‘Friendship-based’ Games” by PJ Lamberson. The agents reach an equilibrium that depends on the strategy played and the topology of the network.

TunaFisher ABM simulates the decisions of fishing companies and fishing vessels of the Philippine tuna purse seinery operating in the Celebes and Sulu Seas.

High fishing effort remains in many of the world’s fisheries, including the Philippine tuna purse seinery, despite a variety of policies that have been implemented to reduce it. These policies have predominantly focused on models of cause and effect which ignore the possibility that the intended outcomes are altered by social behavior of autonomous agents at lower scales.

This model is a spatially explicit Agent-based Model (ABM) for the Philippine tuna purse seine fishery, specifically designed to include social behavior and to study its effects on fishing effort, fish stock and industry profit. The model includes economic and social factors of decision making by companies and fishing vessels that have been informed by interviews.

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FIBE represents a simple fishery model. Fish that reproduce and fisher with different fishing styles that fish as their main source of income. The aim of the model is to reflect the different fishing behaviours as described and observed in the (Swedish) Baltic Sea fishery and explore the consequences of different approximations of human/fisher behaviour in under different environmental and managerial scenarios.

The overarching aim is to advance the incorporation and understanding of human behaviour (diversity) in fisheries research and management. In particular focusing on insights from social (fishery) science of fisher behaviour.

The simulation model conducts fine-grained population projection by specifying life course dynamics of individuals and couples by means of traditional demographic microsimulation and by using agent-based modeling for mate matching.

A model of innovation diffusion in a structured population with two groups who are averse to adopting a produce popular with the outgroup.

Flibs’NLogo is an agent-based simulation implemented in NetLogo that models the evolution of perfect predictors through a genetic algorithm. The agents, called flibs (finite living blobs), are finite‑state automata whose behaviour is encoded in circular chromosomes. They inhabit a “primordial computer soup” and are tasked with anticipating a user‑defined periodic binary sequence. Each generation consists of 100 evaluation cycles, during which a flib’s fitness is incremented each time its output correctly matches the next environmental signal.
Reproduction follows an elitist scheme: a donor (current fittest individual) replaces a randomly chosen recipient either by cloning (complete genome substitution) or by bacterial‑like conjugation (unidirectional horizontal transfer of a random chromosome segment). A stochastic mutagenesis operator introduces point mutations in genes, while the reproductive strategy gene can also switch under a mixed-reproduction regime. Population dynamics are monitored via genomic diversity indices (Shannon‑Wiener, Simpson), a phenotypic simpleness metric that distinguishes the low number of states actually used from the genomic potential.
The model serves as a digital evolutionary laboratory for exploring the interplay among bounded rationality, collective adaptation, and the emergence of anticipatory behaviour. By linking evolutionary computation with cognitive concepts, Flibs’NLogo investigates fundamental transitions from reactive to predictive systems and allows for testing whether populations evolve toward minimal necessary complexity or exhibit an intrinsic drift toward structural elaboration.