Negotiation Lab 1.0 is an agent-based model of peace negotiations that explores how the parties’ readiness — their motivation and optimism to engage in talks — evolves dynamically throughout the negotiation process. The model reconceptualizes readiness as an adaptive state variable that is continuously updated through feedback from negotiation outcomes, rather than a static precondition assessed at the onset of talks.
The model simulates two parties negotiating a multi-issue agenda. In each round, parties allocate effort to the current sub-issue; outcomes depend on their joint effort and a stochastic component representing external factors. Results feed back into each party’s readiness, shaping subsequent engagement. The negotiation ends either when all agenda items are resolved (agreement) or when a party’s readiness falls below a critical threshold (breakdown).
Key parameters include the initial readiness of each party, agenda structure (balanced, hard, easy, red, or random), type of negotiation (from highly cooperative to highly competitive), and each party’s effort strategy (always high, always low, random, or pseudo tit-for-tat). The model shows that while initial readiness is associated with negotiation outcomes, it is neither necessary nor sufficient to determine them: process variables — the type of interaction, agenda design, and adaptive effort strategies — exert comparatively larger effects on outcomes. Identical initial conditions can produce widely divergent trajectories, illustrating path dependence and sensitivity to feedback dynamics.
The model is implemented in NetLogo 7.0 and is documented using the ODD+D protocol. It is associated with the paper “Beyond Initial Conditions: How Adaptive Readiness Shapes Peace Negotiation Outcomes” (Arévalo, under review).

This model visualizes gradient descent optimization - the fundamental algorithm used to train neural networks and other machine learning models. Agents represent different optimization algorithms searching for the minimum of a loss landscape (the “error surface” that ML models try to minimize during training).

The model demonstrates how different optimizer types (SGD, Momentum with different parameters) behave on various loss landscapes, from simple bowls to the notoriously difficult Rosenbrock “banana valley” function. This helps build intuition about why certain optimization algorithms work better than others for different problem geometries.

HOW IT WORKS

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A reproducible NetLogo implementation of a spatial attraction-repulsion opinion model with eviction-driven relocation. Agents interact locally, converge with similar neighbors, diverge from dissimilar neighbors, and may evict the most dissimilar neighbor to a random empty location. Parameter sweeps reveal transitions among extremist, mixed, and consensus regimes, with outputs including phase diagrams, opinion distributions, and Moran’s I. The model is intended to reproduce and extend results on how exclusion frequency changes polarization outcomes.

This model is a minimal agent-based model (ABM) of green consumption and market tipping dynamics in a stylised two-firm economy. It is designed as an existence proof to illustrate how weak individual preferences, when combined with habit formation, social influence, and firm price adaptation, can generate non-linear transitions (tipping points) in market outcomes.

The economy consists of:
1) Two firms, each supplying a differentiated consumption bundle that differs in its fixed green share (one relatively greener, one less green).
2) Many households, each consuming a unit mass per period and allocating consumption between the two firms.

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In his 2003 book, Historical Dynamics (ch. 4), Turchin describes and briefly analyzes a spatial ABM of his metaethnic frontier theory, which is essentially a formalization of a theory by Ibn Khaldun in the 14th century. In the model, polities compete with neighboring polities and can absorb them into an empire. Groups possess “asabiya”, a measure of social solidarity and a sense of shared purpose. Regions that share borders with other groups will have increased asabiya do to salient us vs. them competition. High asabiya enhances the ability to grow, work together, and hence wage war on neighboring groups and assimilate them into an empire. The larger the frontier, the higher the empire’s asabiya.
As an empire expands, (1) increased access to resources drives further growth; (2) internal conflict decreases asabiya among those who live far from the frontier; and (3) expanded size of the frontier decreases ability to wage war along all frontiers. When an empire’s asabiya decreases too much, it collapses.  Another group with more compelling asabiya eventually helps establish a new empire.

This model simulates a forest ecosystem affected by human logging. We explore different kind of approaches and their possible consequences for the ecosystem. Loggers can either be responsible or irresponsible, they will either take care to cut trees or not. In turn their actions will have consequences on the quality of the soil, the atmosphere as well as their profit made from logging. In this model we see that even careful management cannot prevent the degradation of the forest ecosystem.

ARMM is a theoretical agent-based model that formalizes Murra’s Theory of Verticality (Murra, 1972) to explore how multi-zonal resource management systems emerge in mountain landscapes. The model identifies the social, political, and economic mechanisms that enable vertical complementarity across ecological gradients.
Built in NetLogo, ARMM employs an abstract 111×111 grid divided into four Andean ecological zones (Altiplano, Highland, Lowland, Coast), each containing up to 18 resource types distributed according to ecological suitability. To test general theoretical principles rather than replicate specific geography, resource locations are randomized at each model initialization.
Settlement agents pursue one of two economic strategies: diversification (seeking resource variety, maximum 2 units per type) or accumulation (maximising total quantity, maximum 30 units). Agents move between adjacent zones through hierarchical decision-making, first attempting peaceful interactions—coexistence (governed by tolerance) and trading (governed by cooperation)—before resorting to conflict (theft or takeover, governed by belligerence).
The model demonstrates that vertical complementarity can emerge through fundamentally different mechanisms: either through autonomous mobility under political decentralization or through state-coordinated redistribution under centralization. Sensitivity analysis reveals that belligerence and economic strategy explain approximately 25% of outcome variance, confirming that structural inequalities between zones result from political-economic organization rather than environmental constraints alone.
As a preliminary theoretical model, ARMM intentionally maintains simplicity to isolate core mechanisms and generate testable hypotheses. This foundational framework will guide future empirically-calibrated versions that incorporate specific archaeological settlement data and geographic features from the Carangas region (Bolivia-Chile border), enabling direct comparison between theoretical predictions and observed historical patterns.

Boyds (Boids that Fight) is an agent-based model in NetLogo that extends the classic Flocking model with multi-faction competition, a local fight–flight heuristic, and a target locking/“taking” mechanism. The model separates perception (vision) from engagement range (lock distance) and uses per-faction steering bounds to explore how local numerical superiority, sensing, and bounded turning affect victory, losses, and emergent formations.

Software for an agent-based game-theoretic model of the contact hypothesis of prejudice reduction, to accompany “Modeling Prejudice Reduction,” in the Handbook of Computational Social Psychology, adapted from Public Affairs Quarterly 19 (2) 2005.

Subjective biases and errors systematically affect market equilibria, whether at the population level or in bilateral trading. Here, we consider the possibility that an agent engaged in bilateral trading is mistaken about her own valuation of the good she expects to trade, that has not been explicitly incorporated into the existing bilateral trade literature. Although it may sound paradoxical that a subjective private valuation is something an agent can be mistaken about, as it is up to her to fix it, we consider the case in which that agent, seller or buyer, consciously or not, given the structure of a market, a type of good, and a temporary lack of information, may arrive at an erroneous valuation. The typical context through which this possibility may arise is in relation with so-called experience goods, which are sold while all their intrinsic qualities are still unknown (such as untasted bottled fine wines). We model this “private misvaluation” phenomenon in our study. The agents may also be mistaken about how their exchange counterparties are themselves mistaken. Formally, they attribute a certain margin of error to the other agent, which can differ from the actual way that another agent misvalues the good under consideration. This can constitute the source of a second-order misvaluation. We model different attitudes and situations in which agents face unexpected signals from their counterparties and the manner and extent to which they revise their initial beliefs. We analyse and simulate numerically the consequences of first-order and second-order misvaluation on market equilibria.