EffLab was built to support the study of the efficiency of agents in an evolving complex adaptive system. In particular:
- There is a definition of efficiency used in ecology, and an analogous definition widely used in business. In ecological studies it is called EROEI (energy returned on energy invested), or, more briefly, EROI (pronounced E-Roy). In business it is called ROI (dollars returned on dollars invested).
- In addition, there is the more well-known definition of efficiency first described by Sadi Carnot, and widely used by engineers. It is usually represented by the Greek letter ‘h’ (pronounced as ETA). These two measures of efficiency bear a peculiar relationship to each other: EROI = 1 / ( 1 - ETA )

In EffLab, blind seekers wander through a forest looking for energy-rich food. In this multi-generational world, they live and reproduce, or die, depending on whether they can find food more effectively than their contemporaries. Data is collected to measure their efficiency as they evolve more effective search patterns.

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This MAS simulates the traffic of Barcelona Eixample. Uses a centralized AI system in order to control the traffic lights. Car agents are reactive and have no awareness of the intelligence of the system. They (try to) avoid collisions.

We seek to improve understanding of roles enzyme play in soil food webs. We created an agent-based simulation of a simple food web that includes enzymatic activity. The model was used in a publication, Moore et al. (in press; Biochemistry).

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when sharing individuals engage in intentional consumption leveling with a given probability.

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when individuals follow a set of spatially explicit sharing interaction rules derived from a theory on the evolution of the egalitarian social instinct.

The model explores the informational causes of polarization and bi-polarization of opinions in groups. To this end it expands the model of the Argument Communication Theory of Bi-polarization. The latter is an argument-based multi-agent model of opinion dynamics inspired by Persuasive Argument Theory. The original model can account for polarization as an outcome of pure informational influence, and reproduces bi-polarization effects by postulating an additional mechanism of homophilous selection of communication partners. The expanded model adds two dimensions: argument strength and more sophisticated protocols of informational influence (argument communication and opinion update).

The Li-BIM model aims at simulating the behavior of occupants in a building. It is structured around the numerical modeling of the building (IFC format) and a BDI cognitive architecture. The model has been implemented under the GAMA platform.

In this paper we introduce an agent-based model of elections and government formation where voters do not have perfect knowledge about the parties’ ideological position. Although voters are boundedly rational, they are forward-looking in that they try to assess the likely impact of the different parties over the resulting government. Thus, their decision rules combine sincere and strategic voting: they form preferences about the different parties but deem some of them as inadmissible and try to block them from office. We find that the most stable and durable coalition governments emerge at intermediate levels of informational ambiguity. When voters have very poor information about the parties, their votes are scattered too widely, preventing the emergence of robust majorities. But also, voters with highly precise perceptions about the parties will cluster around tiny electoral niches with a similar aggregate effect.

Riparian forests are one of the most vulnerable ecosystems to the development of biological invasions, therefore limiting their spread is one of the main challenges for conservation. The main factors that explain plant invasions in these ecosystems are the capacity for both short- and long-distance seed dispersion, and the occurrence of suitable habitats that facilitate the establishment of the invasive species. Large floods constitute an abiotic filter for invasion.

This model simulates the spatio-temporal spread of the woody invader Gleditsia. triacanthos in the riparian forest of the National Park Esteros de Farrapos e Islas del Río Uruguay, a riparian system in the coast of the Uruguay river (South America). In this model, we represent different environmental conditions for the development of G. triacanthos, long- and short-distance spread of its fruits, and large floods as the main factor of mortality for fruit and early stages.

Field results show that the distribution pattern of this invasive species is limited by establishment, i.e. it spreads locally through the expansion of small areas, and remotely through new invasion foci. This model recreates this dispersion pattern. We use this model to derive management implications to control the spread of G. triacanthos

HousingABM_Japan is a NetLogo agent-based model of the residential market of Tokyo’s 23 wards. It evaluates whether a single parameter configuration can jointly reproduce key features of prices, rents, yields, and market turnover across distinct market regimes from 2001 to 2025, with particular attention to demand- and supply-side trend-following during the 2021–2025 price surge.

The model builds on the Bank of England housing-market lineage (Baptista et al. 2016; Carro et al. 2023) and introduces four extensions: (1) dynamic linkages between the sale and rental markets through vacancy, rents, and yields; (2) heterogeneous demand-side trend-following; (3) supply-side trend-following through construction-cost trend anchoring and a momentum-dependent dynamic premium; and (4) housing-equity borrowing that converts unrealized equity into additional borrowing capacity.

Twenty parameters are calibrated using 2001–2015 data and held fixed for post-calibration evaluation over 2016–2020 and 2021–2025, while annual exogenous inputs follow observed historical paths. The model reproduces the shift from moderate price growth to the 2021–2025 surge, as well as rent acceleration, surge-period yield compression, and persistently low market turnover, although it understates the intermediate acceleration of 2016–2020.

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