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.

A simple model to assess the effect of connectivity on interacting species (i.e. predator-prey type)

A letter sending model with historically informed initial positions to reconstruct communication and archiving processes in the Republic of Letters, the 15th to 17th century form of scholarship.

The model is aimed at historians, willing to formalize historical assumptions about the letter sending process itself and allows in principle to set heterogeneous social roles, e.g. to evaluate the role of gender or social status in the formation of letter exchange networks. The model furthermore includes a pruning process to simulate the loss of letters to critically asses the role of biases e.g. in relation to gender, geographical regions, or power structures, in the creation of empirical letter archives.

Each agent has an initial random topic vector, expressed as a RGB value. The initial positions of the agents are based on a weighted random draw based on data from [2]. In each step, agents generate two neighbourhoods for sending letters and potential targets to move towards. The probability to send letters is a self-reinforcing process. After each sending the internal topic of the receiver is updated as a movement in abstract space by a random amount towards the letters topic.

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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.

The Olympic Peninsula ABM works as a virtual laboratory to simulate the existing forestland management practices as followed by different forestland owner groups in the Olympic Peninsula, Washington, and explore how they could shape the future provisions of multifunctional ecosystem services such as Carbon storage and revenue generation under the business-as-usual scenario as well as by their adaptation to interventions. Forestlands are socio-ecological systems that interact with economic, socio-cultural, and policy systems. Two intervention scenarios were introduced in this model to simulate the adaptation of landowner behavior and test the efficacy of policy instruments in promoting sustainable forest practices and fostering Carbon storage and revenue generation. (1) A market-linked carbon offset scheme that pays the forestland owners a financial incentive in the form of a yearly carbon rent. (2) An institutional intervention policy that allows small forest owners (SFLO) to cooperate for increased market access and benefits under carbon rent scenario. The model incorporates the heterogeneous contexts within which the forestland owners operate and make their forest management decisions by parameterizing relevant agent attributes and contextualizing their unique decision-making processes.

BEGET Classic includes previous versions used in the classroom and for publication. Please check out the latest version of B3GET here, which has several user-friendly features such as directly importing and exporting genotype and population files.

The classic versions of B3GET include: version one and version three were used in undergraduate labs at the University of Minnesota to demonstrate principles in primate behavioral ecology; version two first demonstrated proof of concept for creating virtual biological organisms using decision-vector algorithms; version four was presented at the 2017 annual meeting at the American Association of Physical Anthropologists; version five was presented in a 2019 publication from the Journal of Human Evolution (Crouse, Miller, and Wilson, 2019).

B3GET simulates populations of virtual organisms evolving over generations, whose evolutionary outcomes reflect the selection pressures of their environment. The model simulates several factors considered important in biology, including life history trade-offs, investment in fighting ability and aggression, sperm competition, infanticide, and competition over access to food and mates. Downloaded materials include starting genotype and population files. Edit the these files and see what changes occur in the behavior of virtual populations!

View the B3GET user manual here.