The model simulates interactions in small, task focused groups that might lead to the emergence of status beliefs among group members.

The simulation is a variant of the “ToRealSim OD variants - base v2.7” base model, which is based on the standard DW opinion dynamics model (but with the differences that rather than one agent per tick randomly influencing another, all agents randomly influence one other per tick - this seems to make no difference to the outcomes other than to scale simulation time). Influence can be made one-way by turning off the two-way? switch

Various additional variations and sources of noise are possible to test robustness of outcomes to these (compared to DW model).
In this version agent opinions change following the empirical data collected in some experiments (Takács et al 2016).

Such an algorithm leaves no role for the uncertainties in other OD models. [Indeed the data from (Takács et al 2016) indicates that there can be influence even when opinion differences are large - which violates a core assumption of these]. However to allow better comparison with other such models there is a with-un? switch which allows uncertainties to come into play. If this is on, then influence (according to above algorithm) is only calculated if the opinion difference is less than the uncertainty. If an agent is influenced uncertainties are modified in the same way as standard DW models.

This version adds a Maslowian entropy to each agent decision based on Kendrick et. al. Rudimentary implementation assumes agents with lower scores are more likely to make decisions autonomously rather than sociotropically.

BorealFireSIM is a cellular automaton based model that serves to identify future fire patterns in the boreal forest of Quebec, Canada. The model simulates yearly fire seasons and adjusts decadal climate variables based on two future carbon pathways (RCP45 (low emissions) and RCP85 (business as usual)). The BorealFireSIM model simulates future fire patterns up to the year 2100.

This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission).

We build a computational model to investigate, in an evolutionary setting, a series of questions pertaining to happiness.

In Western countries, the distribution of relative incomes within marriages tends to be skewed in a remarkable way. Husbands usually do not only earn more than their female partners, but there also is a striking discontinuity in their relative contributions to the household income at the 50/50 point: many wives contribute just a bit less than or as much as their husbands, but few contribute more. Our model makes it possible to study a social mechanism that might create this ‘cliff’: women and men differ in their incomes (even outside marriage) and this may differentially affect their abilities to find similar- or higher-income partners. This may ultimately contribute to inequalities within the households that form. The model and associated files make it possible to assess the merit of this mechanism in 27 European countries.

This adaptation of the Relative Agreement model of opinion dynamics (Deffuant et al. 2002) extends the Meadows and Cliff (2012) implementation of this model in a manner that explores the effect of the network structure among the agents.

The purpose of the model is to examine the strength of network connections in a ceremonial exchange network in a non-hierarchical society.

Discriminators who have limited tolerance for helping dissimilar others are necessary for the evolution of costly cooperation in a one-shot Prisoner’s Dilemma. Existing research reports that trust in