The purpose of the Credit and debt market of low-income families model is to help the user examine how the financial market of low-income families works.

The model is calibrated based on real-time data which was collected in a small disadvantaged village in Hungary it contains 159 households’ social network and attributes data.
The simulation models the households’ money liquidity, expenses and revenue structures as well as the formal and informal loan institutions based on their network connections. The model forms an intertwined system integrated in the families’ local socioeconomic context through which families handle financial crises and overcome their livelihood challenges from one month to another.
The simulation-based on the abstract model of low-income families’ financial survival system at the bottom of the pyramid, which was described in following the papers:

…

Within the archeological record for Bronze Age Chinese culture, there continues to be a gap in our understanding of the sudden rise of the Erlitou State from the previous late Longshan chiefdoms. In order to examine this period, I developed and used an agent-based model (ABM) to explore possible socio-politically relevant hypotheses for the gap between the demise of the late Longshan cultures and rise of the first state level society in East Asia. I tested land use strategy making and collective action in response to drought and flooding scenarios, the two plausible environmental hazards at that time. The model results show cases of emergent behavior where an increase in social complexity could have been experienced if a catastrophic event occurred while the population was sufficiently prepared for a different catastrophe, suggesting a plausible lead for future research into determining the life of the time period.

The ABM published here was originally developed in 2016 and its results published in the Proceedings of the 2017 Winter Simulation Conference.

An ABM of changes in individuals’ lifestyles which considers their
evolving behavioural choices. Individuals have a set of environmental behavioural traits that spread through a fixed Watts–Strogatz graph via social interactions with their neighbours. These exchanges are mediated by transmission biases informing from whom an individual learns and
how much attention is paid. The influence of individuals on each other is a function of their similarity in environmental identity, where we represent environmental identity computationally by aggregating past agent attitudes towards multiple environmentally related behaviours. To perform a behaviour, agents must both have
a sufficiently positive attitude toward a behaviour and overcome a corresponding threshold. This threshold
structure, where the desire to perform a behaviour does not equal its enactment, allows for a lack of coherence
between attitudes and actual emissions. This leads to a disconnect between what people believe and what
…

This is a model of plant communities in urban and suburban residential neighborhoods. These plant communities are of interest because they provide many benefits to human residents and also provide habitat for wildlife such as birds and pollinators. The model was designed to explore the social factors that create spatial patterns in biodiversity in yards and gardens. In particular, the model was originally developed to determine whether mimicry behaviors–-or neighbors copying each other’s yard design–-could produce observed spatial patterns in vegetation. Plant nurseries and socio-economic constraints were also added to the model as other potential sources of spatial patterns in plant communities.

The idea for the model was inspired by empirical patterns of spatial autocorrelation that have been observed in yard vegetation in Chicago, Illinois (USA), and other cities, where yards that are closer together are more similar than yards that are farther apart. The idea is further supported by literature that shows that people want their yards to fit into their neighborhood. Currently, the yard attribute of interest is the number of plant species, or species richness. Residents compare the richness of their yards to the richness of their neighbors’ yards. If a resident’s yard is too different from their neighbors, the resident will be unhappy and change their yard to make it more similar.

The model outputs information about the diversity and identity of plant species in each yard. This can be analyzed to look for spatial autocorrelation patterns in yard diversity and to explore relationships between mimicry behaviors, yard diversity, and larger scale diversity.

This model presents the simulation model of a city in the context of overtourism. The study area is the city of Santa Marta in Colombia. The purpose is to illustrate the spatial and temporal distribution of population and tourists in the city. The simulation analyzes emerging patterns that result from the interaction between critical components in the touristic urban system: residents, urban space, touristic sites, and tourists. The model is an Agent-Based Model (ABM) with the GAMA software. Also, it used public input data from statistical centers, geographical information systems, tourist websites, reports, and academic articles. The ABM includes assessing some measures used to address overtourism. This is a field of research with a low level of analysis for destinations with overtourism, but the ABM model allows it. The results indicate that the city has a high risk of overtourism, with spatial and temporal differences in the population distribution, and it illustrates the effects of two management measures of the phenomenon on different scales. Another interesting result is the proposed tourism intensity indicator (OVsm), taking into account that the tourism intensity indicators used by the literature on overtourism have an overestimation of tourism pressures.

This is a simulation of an insurance market where the premium moves according to the balance between supply and demand. In this model, insurers set their supply with the aim of maximising their expected utility gain while operating under imperfect information about both customer demand and underlying risk distributions.

There are seven types of insurer strategies. One type follows a rational strategy within the bounds of imperfect information. The other six types also seek to maximise their utility gain, but base their market expectations on a chartist strategy. Under this strategy, market premium is extrapolated from trends based on past insurance prices. This is subdivided according to whether the insurer is trend following or a contrarian (counter-trend), and further depending on whether the trend is estimated from short-term, medium-term, or long-term data.

Customers are modelled as a whole and allocated between insurers according to available supply. Customer demand is calculated according to a logit choice model based on the expected utility gain of purchasing insurance for an average customer versus the expected utility gain of non-purchase.

This is an agent-based model of a simple insurance market with two types of agents: customers and insurers. Insurers set premium quotes for each customer according to an estimation of their underlying risk based on past claims data. Customers either renew existing contracts or else select the cheapest quote from a subset of insurers. Insurers then estimate their resulting capital requirement based on a 99.5% VaR of their aggregate loss distributions. These estimates demonstrate an under-estimation bias due to the winner’s curse effect.

The targeted subsidies plan model is based on the economic concept of targeted subsidies.

The targeted subsidies plan model simulates the distribution of subsidies among households in a community over several years. The model assumes that the government allocates a fixed amount of money each year for the purpose of distributing cash subsidies to eligible households. The eligible households are identified by dividing families into 10 groups based on their income, property, and wealth. The subsidy is distributed to the first four groups, with the first group receiving the highest subsidy amount. The model simulates the impact of the subsidy distribution process on the income and property of households in the community over time.

The model simulates a community of 230 households, each with a household income and wealth that follows a power-law distribution. The number of household members is modeled by a normal distribution. The model allocates a fixed amount of money each year for the purpose of distributing cash subsidies among eligible households. The eligible households are identified by dividing families into 10 groups based on their income, property, and wealth. The subsidy is distributed to the first four groups, with the first group receiving the highest subsidy amount.
The model runs for a period of 10 years, with the subsidy distribution process occurring every month. The subsidy received by each household is assumed to be spent, and a small portion may be saved and added to the household’s property. At the end of each year, the grouping of households based on income and assets is redone, and a number of families may be moved from one group to another based on changes in their income and property.
…

What policy measures are effective in redistributing essential resources during crisis situations such as climate change impacts? We model a collective action institution with different rules for designing and organizing it, and make our analysis specific to various societal contexts.

Our model captures a generic societal context of unequal vulnerability and climate change impact in a stylized form. We represent a community of people who harvest and consume an essential resource to maintain their well-being. However, their ability to harvest the resource is not equal; people are characterized by a ‘resource access’ attribute whose values are uniformly distributed from 0 to 1 in the population. A person’s resource access value determines the amount of resource units they are able to harvest, and therefore the welfare levels they are able to attain. People travel to the centralized resource region and derive well-being or welfare, represented as an energy gain, by harvesting and consuming resource units.

The community is subject to a climate change impact event that occurs with a certain periodicity and over a certain duration. The capacity of resource units to regenerate diminishes during the impact events. Unequal capacities to access the essential resource results in unequal vulnerability among people with regards to their ability to maintain a sufficient welfare level, especially during impact events.

…

The model simulates the diffusion of four low-carbon energy technologies among households: photovoltaic (PV) solar panels, electric vehicles (EVs), heat pumps, and home batteries. We model household decision making as the decision marking of one person, the agent. The agent decides whether to adopt these technologies. Hereby, the model can be used to study co-adoption behaviour, thereby going beyond traditional diffusion models that focus on the adop-tion of single technologies. The combination of these technologies is of particular interest be-cause (1) using the energy generated by PV solar panels for EVs and heat pumps can reduce emissions associated with transport and heating, respectively, and (2) EVs, heat pumps, and home batteries can help to integrate PV solar panels in local electricity grids by offering flexible demand (EVs and heat pumps) and energy storage (home batteries and EVs), thereby reducing grid impacts and associated upgrading costs.

The purpose of the model is to represent realistic adoption and co-adoption behaviour. This is achieved by grounding the decision model on the risks-as-feelings model (Loewenstein et al., 2001), theory from environmental and social psychology, and empirically informing agent be-haviour by survey-data among 1469 people in the Swiss region Romandie.

The model can be used to construct scenarios for the diffusion of the four low-carbon energy technologies depending on different contexts, and as a virtual experimentation environment for ex ante evaluation of policy interventions to stimulate adoption and co-adoption.