This model is an application of Brantingham’s neutral model to a real landscape with real locations of potential sources. The sources are represented as their sizes during current conditions, and from marine geophysics surveys, and the agent starts at a random location in Mossel Bay Region (MBR) surrounding the Archaeological Pinnacle Point (PP) locality, Western Cape, South Africa. The agent moves at random on the landscape, picks up and discards raw materials based only upon space in toolkit and probability of discard. If the agent happens to encounter the PP locality while moving at random the agent may discard raw materials at it based on the discard probability.

In this Repast model the ‘Consumat’ cognitive framework is applied to an ABM of the Dutch car market. Different policy scenarios can be selected or created to examine their effect on the diffusion of EVs.

We propose an agent-based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the bargaining model by Axtell, Epstein and Young.

In this agent-based model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood.

Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect.

The model represents a large, traditional firm competing with small, sustainable food producers. The large firm operates under conventional assumptions, pursuing monopoly profits maximized through standard economic principles. Its behavior is driven by economies of scale, which compel small firms to strategically differentiate themselves to deliver added value to consumers. Consequently, small firms focus on offering higher-quality products—a factor that matters significantly to consumers.

The gains achieved by small firms represent social profits that hinge upon establishing co-participation schemes with farmers and building trustworthy relationships with consumers. Cooperation with farmers directly shapes production processes and product quality. Because a firm trusted by one consumer becomes visible to neighbors, trust can diffuse and evolve into a collective identity. Ultimately, population-level legitimation occurs when the organizational form of the organic food producer gains widespread acceptance within the consumer community.

The model’s behavior depends on three key factors: a trust parameter, a legitimation intensity parameter, and an aspiration-level weight (for the large firm).

The model explores how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect ant colony level foraging in a variable enviroment.

HOSS(-5) is an exploratory agent-based prototype of a small artificial society, written in Python. Each agent is a “ph” (punto humano, “human point”) that is born, is educated, works, consumes and dies. Its yearly decisions depend on a psychological profile (archetype × character), its economic status and its intentionality. Agents interact indirectly through families, firms in 10 sectors, a labour market with about 200 job categories, and a State that provides public services, employment and a Universal Basic Income, adjusting taxes and UBI each year to seek fiscal balance.

The model simulates a few thousand agents over decades of virtual time and records yearly aggregates (population, GDP, income, Gini index, HDI). A separate analysis script explores the results and fits Gamma distributions to individual incomes, comparing the shape parameter with Gini and HDI.

HOSS does not aim to predict real societies or claim empirical validation. It is a sandbox for comparing scenarios and thinking about the organizational design of society from an engineering perspective. Known limitations: no direct social relations between agents, prices and production follow simple rules rather than supply-demand dynamics, and organizations are black boxes.

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This model tests whether local housing supply elasticity governs crash severity inside a single metropolitan housing market. Saiz (2010) established that across US metros, regions constrained by geography and regulation experience deeper boom-bust cycles than flexible ones. That finding is routinely applied downward to neighborhoods and ZIP codes as though the mechanism scaled without qualification.

The empirical record for the Washington DC and Northern Virginia region says it does not. Across 84 ZIP codes, measured supply elasticity ranges from 0.35 to 4.95 with a median of 1.21. The worst single-year price decline between 2007 and 2012 averaged 9.6 percent in constrained ZIP codes and 9.0 percent in flexible ones, a gap that cannot be distinguished from noise. Wide variation in the proposed cause, no meaningful separation in the proposed effect.

The model embeds households, houses and a metro-wide credit condition in the real ZIP geography of the region using three GIS layers and an empirical price panel. Local elasticity governs construction, exactly as theory predicts. Prices are driven by a shared macro drift schedule and, under the credit-amplified mode, by a leverage cycle with a financial accelerator and a deviation penalty. The design question is whether those shared forces are sufficient to override local supply differences at the sub-metropolitan scale.

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This model represents informal information transmission networks among medieval Genoese investors used to inform each other about cheating merchants they employed as part of long-distance trade operations.

Inspired by the European project called GLODERS that thoroughly analyzed the dynamics of extortive systems, Bottom-up Adaptive Macroeconomics with Extortion (BAMERS) is a model to study the effect of extortion on macroeconomic aggregates through simulation. This methodology is adequate to cope with the scarce data associated to the hidden nature of extortion, which difficults analytical approaches. As a first approximation, a generic economy with healthy macroeconomics signals is modeled and validated, i.e., moderate inflation, as well as a reasonable unemployment rate are warranteed. Such economy is used to study the effect of extortion in such signals. It is worth mentioning that, as far as is known, there is no work that analyzes the effects of extortion on macroeconomic indicators from an agent-based perspective. Our results show that there is significant effects on some macroeconomics indicators, in particular, propensity to consume has a direct linear relationship with extortion, indicating that people become poorer, which impacts both the Gini Index and inflation. The GDP shows a marked contraction with the slightest presence of extortion in the economic system.