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.

We present the Integrated Urban Complexity model (IUCm 1.0) that computes “climate-smart urban forms”, which are able to cut emissions related to energy consumption from urban mobility in half. Furthermore, we show the complex features that go beyond the normal debates about urban sprawl vs. compactness. Our results show how to reinforce fractal hierarchies and population density clusters within climate risk constraints to significantly decrease the energy consumption of urban mobility. The new model that we present aims to produce new advice about how cities can combat climate change. From a technical angle, this model is a geographical automaton, conceptually interfacing between cellular automata and spatial explicit optimisation to achieve normative sustainability goals related to low energy. See a complete user guide at https://iucm.readthedocs.io/en/latest/ .

An agent-based framework that aggregates social network-level individual interactions to run targeting and rewarding programs for a freemium social app. Git source code in https://bitbucket.org/mchserrano/socialdynamicsfreemiumapps

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 MML is a hybrid modeling environment that couples an agent-based model of small-holder agropastoral households and a cellular landscape evolution model that simulates changes in erosion/deposition, soils, and vegetation.

This model is to explore the changes of paddy field landscape and household livelihood structure in the village under different policy scenarios, evaluate the eco-social effects of different policies, and provide decision support tools for proposing effective and feasible policies.

SimAdapt: An individual-based genetic model for simulating landscape management impacts on populations

ViSA simulates the decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. The lack of sufficient supply of ESSs triggers stakeholders to apply different management options to increase their supply. However, while attempting to reduce the supply-demand gap, conflicts arise among stakeholders due to the tradeoff nature of some ESS. ViSA investigates conditions and scenarios that can minimize such supply-demand gap while reducing the risk of conflicts by suggesting different mixes of management options and decision rules.

InformalCity, a spatially explicit agent-based model, simulates an artificial city and allows for testing configurations of urban upgrading schemes in informal settlements.

This model was built to estimate the impacts of exogenous fodder input and credit loans services on livelihood, rangeland health and profits of pastoral production in a small holder pastoral household in the arid steppe rangeland of Inner Mongolia, China. The model simulated the long-term dynamic of herd size and structure, the forage demand and supply, the cash flow, and the situation of loan debt under three different stocking strategies: (1) No external fodder input, (2) fodders were only imported when natural disaster occurred, and (3) frequent import of external fodder, with different amount of available credit loans. Monte-Carlo method was used to address the influence of climate variability.