Computational Model Library

Peer reviewed MOOvPOPsurveillance

Aniruddha Belsare Matthew Gompper Joshua J Millspaugh | Published Tue Apr 4 17:03:40 2017 | Last modified Tue May 12 16:37:24 2020

MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.

This model is an extension of the Artificial Long House Valley (ALHV) model developed by the authors (Swedlund et al. 2016; Warren and Sattenspiel 2020). The ALHV model simulates the population dynamics of individuals within the Long House Valley of Arizona from AD 800 to 1350. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. The present version of the model incorporates features of the ALHV model including realistic age-specific fertility and mortality and, in addition, it adds the Black Mesa environment and population, as well as additional methods to allow migration between the two regions.

As is the case for previous versions of the ALHV model as well as the Artificial Anasazi (AA) model from which the ALHV model was derived (Axtell et al. 2002; Janssen 2009), this version makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original AA model to estimate annual maize productivity of various agricultural zones within the Long House Valley. A new environment and associated methods have been developed for Black Mesa. Productivity estimates from both regions are used to determine suitable locations for households and farms during each year of the simulation.

Lethal Geometry

Kristin Crouse | Published Fri Feb 21 11:27:16 2020

Lethal Geometry examines the relationship between territory size and intergroup mortality risk under realistic assumptions. Furthermore, the model investigates how fertility is affected by this relationship. Territory sizes are expected to fluctuate over time in response to individual reproduction, random-walking, and lethal intergroup encounters. In turn, the individuals within these territories are expected to vary in their mortality and fertility rates.

This study investigates a possible nexus between inter-group competition and intra-group cooperation, which may be called “tribalism.” Building upon previous studies demonstrating a relationship between the environment and social relations, the present research incorporates a social-ecological model as a mediating factor connecting both individuals and communities to the environment. Cyclical and non-cyclical fluctuation in a simple, two-resource ecology drive agents to adopt either “go-it-alone” or group-based survival strategies via evolutionary selection. Novelly, this simulation employs a multilevel selection model allowing group-level dynamics to exert downward selective pressures on individuals’ propensity to cooperate within groups. Results suggest that cooperation and inter-group conflict are co-evolved in a triadic relationship with the environment. Resource scarcity increases inter-group competition, especially when resources are clustered as opposed to widely distributed. Moreover, the tactical advantage of cooperation in the securing of clustered resources enhanced selective pressure on cooperation, even if that implies increased individual mortality for the most altruistic warriors. Troubling, these results suggest that extreme weather, possibly as a result of climate change, could exacerbate conflict in sensitive, weather-dependent social-ecologies—especially places like the Horn of Africa where ecologically sensitive economic modalities overlap with high-levels of diversity and the wide-availability of small arms. As well, global development and foreign aid strategists should consider how plans may increase the value of particular locations where community resources are built or aid is distributed, potentially instigating tribal conflict. In sum, these factors, interacting with pre-existing social dynamics dynamics, may heighten inter-ethnic or tribal conflict in pluralistic but otherwise peaceful communities.

For special issue submission in JASSS.

Wedding Doughnut

Eric Silverman Jakub Bijak Jason Hilton Viet Cao | Published Thu Dec 20 16:04:09 2012 | Last modified Fri Sep 20 11:42:53 2013

A reimplementation of the Wedding Ring model by Francesco Billari. We investigate partnership formation in an agent-based framework, and combine this with statistical demographic projections using real empirical data.

ForagerNet3_Demography_V3

Andrew White | Published Tue Nov 29 19:47:29 2016

The ForagerNet3_Demography model is a non-spatial ABM designed to serve as a platform for exploring several aspects of hunter-gatherer demography.

Population Dynamics of Emerald Ash Borer

mpeters | Published Mon Dec 13 14:21:15 2010 | Last modified Sat Apr 27 20:18:43 2013

This model was developed as part of a class project, and explores the population dynamics and spread of an invasive insect, Emerald Ash Borer, in a county.

Peer reviewed Family Herd Demography

Abigail Buffington Andrew Yoak Ian M Hamilton Rebecca Garabed Mark Moritz | Published Mon Aug 15 19:41:24 2016 | Last modified Sat Jan 6 15:27:40 2018

The model examines the dynamics of herd growth in African pastoral systems. We used it to examine the role of scale (herd size) stochasticity (in mortality, fertility, and offtake) on herd growth.

ForagerNet3_Demography: A Non-Spatial Model of Hunter-Gatherer Demography

Andrew White | Published Thu Oct 17 18:53:03 2013 | Last modified Thu Oct 17 19:13:37 2013

ForagerNet3_Demography is a non-spatial ABM for exploring hunter-gatherer demography. Key methods represent birth, death, and marriage. The dependency ratio is an imporant variable in many economic decisions embedded in the methods.

ForagerNet3_Demography_V2

Andrew White | Published Thu Feb 13 16:06:26 2014

ForagerNet3_Demography_V2 is a non-spatial ABM for exploring hunter-gatherer demography. This version (developed from FN3D_V1) contains code for calculating the ratio of old to young adults (the “OY ratio”) in the living and dead populations.

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