The purpose of this model is to explain the post-disaster recovery of households residing in their own single-family homes and to predict households’ recovery decisions from drivers of recovery. Herein, a household’s recovery decision is repair/reconstruction of its damaged house to the pre-disaster condition, waiting without repair/reconstruction, or selling the house (and relocating). Recovery drivers include financial conditions and functionality of the community that is most important to a household. Financial conditions are evaluated by two categories of variables: costs and resources. Costs include repair/reconstruction costs and rent of another property when the primary house is uninhabitable. Resources comprise the money required to cover the costs of repair/reconstruction and to pay the rent (if required). The repair/reconstruction resources include settlement from the National Flood Insurance (NFI), Housing Assistance provided by the Federal Emergency Management Agency (FEMA-HA), disaster loan offered by the Small Business Administration (SBA loan), a share of household liquid assets, and Community Development Block Grant Disaster Recovery (CDBG-DR) fund provided by the Department of Housing and Urban Development (HUD). Further, household income determines the amount of rent that it can afford. Community conditions are assessed for each household based on the restoration of specific anchors. ASNA indexes (Nejat, Moradi, & Ghosh 2019) are used to identify the category of community anchors that is important to a recovery decision of each household. Accordingly, households are indexed into three classes for each of which recovery of infrastructure, neighbors, or community assets matters most. Further, among similar anchors, those anchors are important to a household that are located in its perceived neighborhood area (Moradi, Nejat, Hu, & Ghosh 2020).

The model simulates the spread of a virus through a synthetic network with a degree distribution calibrated on close-range contact data. The model is used to study the macroscopic consequences of cross-individual variability in close-range contact frequencies and to assess whether this variability can be exploited for effective intervention targeting high-contact nodes.

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when sharing individuals engage in intentional consumption leveling with a given probability.

The fight against poverty is an urgent global challenge. Microinsurance is promoted as a valuable instrument for buffering income losses due to health or climate-related risks of low-income households in developing countries. However, apart from direct positive effects they can have unintended side effects when insured households lower their contribution to traditional arrangements where risk is shared through private monetary support.

RiskNetABM is an agent-based model that captures dynamics between income losses, insurance payments and informal risk-sharing. The model explicitly includes decisions about informal transfers. It can be used to assess the impact of insurance products and informal risk-sharing arrangements on the resilience of smallholders. Specifically, it allows to analyze whether and how economic needs (i.e. level of living costs) and characteristics of extreme events (i.e. frequency, intensity and type of shock) influence the ability of insurance and informal risk-sharing to buffer income shocks. Two types of behavior with regard to private monetary transfers are explicitly distinguished: (1) all households provide transfers whenever they can afford it and (2) insured households do not show solidarity with their uninsured peers.

The model is stylized and is not used to analyze a particular case study, but represents conditions from several regions with different risk contexts where informal risk-sharing networks between smallholder farmers are prevalent.

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The objective of building a social simulation in the Populism and Civic Engagement (PaCE) project is to study the phenomenon of populism by mapping individual level political behaviour and explain the influence of agents on, and their interdependence with the respective political parties. Voters, political parties and – to some extent – the media can be viewed as forming a complex adaptive system, in which parties compete for citizens’ votes, voters decide on which party to vote for based on their respective positions with regard to particular issues, and the media may influence the salience of issues in the public debate.

This is the first version of a model exploring voting behaviour in Austria. It focusses on modelling the interaction of voters and parties in a political landscape; the effects of the media are not yet represented. Austria was chosen as a case study because it has an established populist party (the “Freedom Party” FPO), which has even been part of the government over the years.

The community consequences of intra-specific trait variation (ITV) are a current topic in ecological research. The effects of ITV on species coexistence have, yet, not sufficiently been understood. With this individual-based model we analyzed the effect of intra-specific variation in movement by mimicking variation found in ground-dwelling rodents and analyzing how such variation affects inter-specific differences in competitive ability (i.e. foraging efficiency) and temporary coexistence. The movement algorithm and behavioral plasticity was adapted from existing algorithms and current ecological literature. As a measure for temporary coexistence, we analyzed the time until one of the species went extinct.

This version of the accumulated copying error (ACE) model is designed to address the following research question: how does finite population size (N) affect the coefficient of variation (CV) of a continuous cultural trait under the assumptions that the only source of copying error is visual perception error and that the continuous trait can take any positive value (i.e., it has no upper bound)? The model allows one to address this question while assuming the continuous trait is transmitted via vertical transmission, unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission. By varying the parameter, p, one can also investigate the effect of population size under a mix of vertical and non-vertical transmission, whereby on average (1-p)N individuals learn via vertical transmission and pN individuals learn via either unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission.

NetLogo model that allows scenarios concerning general social distancing, shielding of high-risk individuals, and informing contacts when symptomatic. Documentation includes a user manual with some simple scenarios, and technical information including descriptions of key procedures and parameter values.

After a little work experience, we realize that different kinds of people prefer different work environments: some enjoy a fast-paced challenge; some want to get by; and, others want to show off.

From that experience, we also realize that different kinds of people affect their work environments differently: some increase the pace; some slow it down; and, others make it about themselves.

This model concerns how three different kinds of people affect their work environment and how that work environment affects them in return. The model explores how this circular relation between people’s preferences and their environment creates patterns of association and performance over time.

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This is a set of threshold public goods games models. Set consists of baseline model, endogenous shared punishment model, endogenous shared punishment model with activists and cooperation model. In each round, all agents are granted a budget of size set in GUI. Then they decide on how much they contribute to public goods and how much they keep. Public goods are provided only if the sum of contributions meets or exceeds the threshold defined in the GUI. After each round agents evaluate their strategy and payoff from this strategy.