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.

Norms@Risk ABM aim is to develop an explanation for the conditions under which people jointly take action to prevent disasters from happening, e.g. impacts from climate crisis, pandemic. The aim of the Norms@Risk work is to explore understudied dynamics between risk, social norms and cooperative behaviour and their role in overcoming collective threats. More specifically, Norms@Risk seeks to refine existing theory on cooperation by unpacking the role of collective risk interacting with social norms in ‘Collective Risk Social Dilemmas’*.

This simulation study takes on the next scientific iteration after a series of behavioural experiments. With the model we target to refine existing theory by capturing the heterogeneity in contributions that existing theories cannot fully explain as the behavioural responses are assumed to be more homogenous, particularly diversity regarding sensitivity to risk and norms.

*Collective Risk Social Dilemmas reflect a special type of social dilemma, unlike typical public goods, the collective risk dilemma involves individual contribution not to realise a gain, but to avoid a collective loss.

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The model is an experimental ground to study the impact of network structure on diffusion. It allows to construct a social network that already has some measurable level of homophily, and simulate a diffusion process over this social network.

This generic agent-based model allows the user to simulate and explore the influence of servicising policies on the uptake of servicising and on economic, environmental and social effects, notably absolute decoupling.

Our societal belief systems are pruned by evolution, informing our unsustainable economies. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.

Innovation a byproduct of the intellectual capital, requires a new paradigm for the production constituents. Human Capital HC,Structural capital SC and relational capital RC become key for intellectual capital and consequently for innovation.

This model is an extension of Wilensky’s (2003) Traffic Grid, a foundational NetLogo model of urban traffic flow. It embeds a dual-process cognitive architecture into each driver agent, transforming the original’s identical reactive units into cognitively heterogeneous individuals whose internal mental state evolves with experience; making the same intersection produce different decisions from different drivers, and different decisions from the same driver across occasions.
The core question the model addresses is the yellow-light dilemma zone: the seconds following amber onset in which a driver can neither stop safely nor clear the intersection before red. Field research documents that behavioral variance at this moment cannot be explained by geometry or legal obligation alone. This model provides the cognitive architecture that has been missing from traffic ABMs.
Each driver routes every amber-onset decision through either System 1 (fast, heuristic, automatic) or System 2 (slow, deliberative, prospect-theoretic), switching dynamically based on cognitive load, accumulated near-miss memory, and situational framing. The result is crash outcomes that are attributable, path-dependent, and sensitive to both driver disposition and signal infrastructure; none of which fixed-rule models can reproduce. Three signal control modes are included: fixed-cycle (replicating Wilensky’s original), adaptive-queue, and smart occupancy-based switching.

This is an agent-based model coded in NetLogo. The model simulates population dynamics of bighorn sheep population in the Hell’s Canyon region of Idaho and will be used to develop a better understanding of pneumonia dynamics in bighorn sheep populations. The overarching objective is to provide a decision-making context for effective management of pneumonia in wild populations of bighorn sheep.

This model is a spatial evolutionary game theory model in which animals with inherited red or blue phenotypes move, interact locally, gain or lose energy based on payoff structure, and reproduce across generations. Plants provide a stationary green component of the environment. The model can be used to examine how game payoffs, inheritance, and survival shape population and evolutionary dynamics over time.

Our Hybrid Climate Assessment Model (HCAM) aims to simulate the behaviours of individuals under the influence of climate change and external policy makings. In our proposed solution we use System Dynamics (SD) modelling to represent the physical and economic environments. Agent-Based (AB) modelling is used to represent collections of individuals that can interact with other collections of individuals and the environment. In turn, individual agents are endowed with an internal SD model to track their psychological state used for decision making. In this paper we address the feasibility of such a scalable hybrid approach as a proof-of-concept. This novel approach allows us to reuse existing rigid, but well-established Integrated Assessment Models (IAMs), and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities.

Our illustrative example takes the settings of the U.S., a country that contributes to the majority of the global carbon footprints and that is the largest economic power in the world. The model considers the carbon emission dynamics of individual states and its relevant economic impacts on the nation over time.

Please note that the focus of the model is on a methodological advance rather than on applying it for predictive purposes! More details about the HCAM are provided in the forthcoming JASSS paper “An Innovative Approach to Multi-Method Integrated Assessment Modelling of Global Climate Change”, which is available upon request from the authors (contact [email protected]).