Computational Model Library

A road freight transport (RFT) operation involves the participation of several types of companies in its execution. The TRANSOPE model simulates the subcontracting process between 3 types of companies: Freight Forwarders (FF), Transport Companies (TC) and self-employed carriers (CA). These companies (agents) form transport outsourcing chains (TOCs) by making decisions based on supplier selection criteria and transaction acceptance criteria. Through their participation in TOCs, companies are able to learn and exchange information, so that knowledge becomes another important factor in new collaborations. The model can replicate multiple subcontracting situations at a local and regional geographic level.
The succession of n operations over d days provides two types of results: 1) Social Complex Networks, and 2) Spatial knowledge accumulation environments. The combination of these results is used to identify the emergence of new logistics clusters. The types of actors involved as well as the variables and parameters used have their justification in a survey of transport experts and in the existing literature on the subject.
As a result of a preferential selection process, the distribution of activity among agents shows to be highly uneven. The cumulative network resulting from the self-organisation of the system suggests a structure similar to scale-free networks (Albert & Barabási, 2001). In this sense, new agents join the network according to the needs of the market. Similarly, the network of preferential relationships persists over time. Here, knowledge transfer plays a key role in the assignment of central connector roles, whose participation in the outsourcing network is even more decisive in situations of scarcity of transport contracts.

The model measures drivers of effectiveness of risk assessments in risk workshops where a calculative culture of quantitative skepticism is present. We model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops, in order to contrast results to a previous model focused on a calculative culture of quantitative enthusiasm.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use constraint satisfaction networks to assess a given risk individually and as a group.

The model measures drivers of effectiveness of risk assessments in risk workshops regarding the correctness and required time. Specifically, we model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use Bayesian networks to assess a given risk individually and as a group.

This project was developed during the Santa Fe course Introduction to Agent-Based Modeling 2022. The origin is a Cellular Automata (CA) model to simulate human interactions that happen in the real world, from Rubens and Oliveira (2009). These authors used a market research with real people in two different times: one at time zero and the second at time zero plus 4 months (longitudinal market research). They developed an agent-based model whose initial condition was inherited from the results of the first market research response values and evolve it to simulate human interactions with Agent-Based Modeling that led to the values of the second market research, without explicitly imposing rules. Then, compared results of the model with the second market research. The model reached 73.80% accuracy.
In the same way, this project is an Exploratory ABM project that models individuals in a closed society whose behavior depends upon the result of interaction with two neighbors within a radius of interaction, one on the relative “right” and other one on the relative “left”. According to the states (colors) of neighbors, a given cellular automata rule is applied, according to the value set in Chooser. Five states were used here and are defined as levels of quality perception, where red (states 0 and 1) means unhappy, state 3 is neutral and green (states 3 and 4) means happy.
There is also a message passing algorithm in the social network, to analyze the flow and spread of information among nodes. Both the cellular automaton and the message passing algorithms were developed using the Python extension. The model also uses extensions csv and arduino.

Resilience of humans in the Upper Paleolithic could provide insights in how to defend against today’s environmental threats. Approximately 13,000 years ago, the Laacher See volcano located in present-day western Germany erupted cataclysmically. Archaeological evidence suggests that this is eruption – potentially against the background of a prolonged cold spell – led to considerable culture change, especially at some distance from the eruption (Riede, 2017). Spatially differentiated and ecologically mediated effects on contemporary social networks as well as social transmission effects mediated by demographic changes in the eruption’s wake have been proposed as factors that together may have led to, in particular, the loss of complex technologies such as the bow-and-arrow (Riede, 2014; Riede, 2009).

This model looks at the impact of the interaction between climate change trajectory and an extreme event, such as the Laacher See eruption, on the generational development of hunter-gatherer bands. Historic data is used to model the distribution and population dynamics of hunter-gatherer bands during these circumstances.

This model aims to explore how gambling-like behavior can emerge in loot box spending within gaming communities. A loot box is a purchasable mystery box that randomly awards the player a series of in-game items. Since the contents of the box are largely up to chance, many players can fall into a compulsion loop of purchasing, as the fear of missing out and belief in the gambler’s fallacy allow one to rationalize repeated purchases, especially when one compares their own luck to others. To simulate this behavior, this model generates players in different network structures to observe how factors such as network connectivity, a player’s internal decision making strategy, or even common manipulations games use these days may influence a player’s transactions.

Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.

The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:

  1. Network generation: At the start of the simulation, the model generates a small world network of social links between the customers, and randomly assigns each customer to an initial insurer
  2. ...

Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

This model is developed as a theoretical agent-based model to study the general phenomena of network-based targeting strategies on eco-innovation adoption and diffusion through inter-firm networks.

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