Organisms, Individuals and Organizations face the dilemma of exploration vs. exploitation
Identifying the optimal trade-off between the two is a challenge
Too much exploration (e.g. gaining new knowledge) can be detrimental to day-to-day survival and too much exploitation (applying existing knowledge) could be detrimental to long term survival esp. if conditions change over time

The purpose of the model is to investigate how the amount of resources acquired (wealth/success) is related to persistence with the strategy of local exploration under different resource distributions, availability of resources over time and cost of relocation

Indirect reciprocity can be evolved by the shared information among the people of small subgroups in the population.

This is extended version of the MERCRUY model (Brughmans 2015) incorporates a ‘transport-cost’ variable, and is otherwise unchanged. This extended model is described in this publication: Brughmans, T., 2019. Evaluating the potential of computational modelling for informing debates on Roman economic integration, in: Verboven, K., Poblome, J. (Eds.), Structural Determinants in the Roman World.

Brughmans, T., 2015. MERCURY: an ABM of tableware trade in the Roman East. CoMSES Comput. Model Libr. URL https://www.comses.net/codebases/4347/releases/1.1.0/

A draft model with some useful code for creating different network structures using the Netlogo NW extension. This model is used for the following tutorial:
Brughmans, T. (2018). Network structures and assembling code in Netlogo, Tutorial, https://archaeologicalnetworks.wordpress.com/resources/#structures .

A draft model teaching how a Roman transport model can be imported into Netlogo, and the issues confronted when importing and reusing open access Roman datasets. This model is used for the tutorial:
Brughmans, T. (2018). Importing a Roman Transport network with Netlogo, Tutorial, https://archaeologicalnetworks.wordpress.com/resources/#transport .

This model implements a combined Protective Action Decision Model (PADM) and Protection Motivation Theory (PAM) model for human decision making regarding hazard mitigations. The model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It was validated against FEMA damage assessments and post-storm surveys (O’Neil 2017).

This model is an extended version of the original MERCURY model (https://www.comses.net/codebases/4347/releases/1.1.0/ ) . It allows for experiments to be performed in which empirically informed population sizes of sites are included, that allow for the scaling of the number of tableware traders with the population of settlements, and for hypothesised production centres of four tablewares to be used in experiments.

Experiments performed with this population extension and substantive interpretations derived from them are published in:

Hanson, J.W. & T. Brughmans. In press. Settlement scale and economic networks in the Roman Empire, in T. Brughmans & A.I. Wilson (ed.) Simulating Roman Economies. Theories, Methods and Computational Models. Oxford: Oxford University Press.

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UPDATE in V1.1.0: missing input data files added; relative paths to input data files changed to “../data/FILENAME”

A model that allows for representing key theories of Roman amphora reuse, to explore the differences in the distribution of amphorae, re-used amphorae and their contents.

This model generates simulated distributions of prime-use amphorae, primeuse contents (e.g. olive oil) and reused amphorae. These simulated distributions will differ between experiments depending on the experiment’s variable settings representing the tested theory: variations in the probability of reuse, the supply volume, the probability of reuse at ports. What we are interested in teasing out is what the effect is of each theory on the simulated amphora distributions.

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This work is a java implementation of a study of the viability of a population submitted to floods. The population derives some benefit from living in a certain environment. However, in this environment, floods can occur and cause damage. An individual protection measure can be adopted by those who wish and have the means to do so. The protection measure reduces the damage in case of a flood. However, the effectiveness of this measure deteriorates over time. Individual motivation to adopt this measure is boosted by the occurrence of a flood. Moreover, the public authorities can encourage the population to adopt this measure by carrying out information campaigns, but this comes at a cost. People’s decisions are modelled based on the Protection Motivation Theory (Rogers1975, Rogers 1997, Maddux1983) arguing that the motivation to protect themselves depends on their perception of risk, their capacity to cope with risk and their socio-demographic characteristics.
While the control designing proper informations campaigns to remain viable every time is computed in the work presented in https://www.comses.net/codebases/e5c17b1f-0121-4461-9ae2-919b6fe27cc4/releases/1.0.0/, the aim of the present work is to produce maps of probable viability in case the serie of upcoming floods is unknown as well as much of the parameters for the population dynamics. These maps are bi-dimensional, based on the value of known parameters: the current average wealth of the population and their actual or possible future annual revenues.

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.