An agent-based model of one colony of the Florida harvester ant, Pogonomyrmex badius, from the day a mated queen lands to the death of the colony. It runs in a web browser and, unchanged, headless in Node for replicate studies.

What the ants do: a claustral queen digs her own shaft and raises the first workers on her reserves; workers dig the nest under local rules, with no ant holding a plan; a one-way, age-based division of labour set by the season of birth, which no shortage reverses; trunk-trail foraging with site fidelity and recruitment; seed storage, opening and germination in the chambers, which is what feeds the larvae; daily weather from the climate normals of the study site, with drought years; nuptial flights after heavy rain; corpse removal; alarm at a disturbance on the foraging ground; and annual nest relocation, in which the store and then the brood are carried along the trail to a new nest the colony digs.

Every value in the model is tagged as measured in this species, borrowed from another ant, or invented, with its source, in a single parameter file. The model is deterministic: one seeded stream per system and a fixed one-minute timestep, so a seed reproduces a run bit for bit in the browser and in Node, which a test pins. A study writes a methods report listing the invented values the results rest on and the acceptance tests the model fails.

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The Relation-Based Model (RBM) purpose is to operationalise (a form of) process-relational (PR) thinking to serve as a thinking tool for process-relational thinking among social-ecological system (SES) researchers. The development of this model itself has been a ‘Proof of concept’- exercise to see whether we actually represent process-relational thinking in a methodology that is entity-based (ABM).

The target of the agent-based model is to show the emergence, change and disappearance of fishing assemblages (focusing on processes of self-organisation) in a Mexican fishery using a process-relational view. From this view, a fishery is regarded as an assemblage in which fishing can be enabled, fishing can occur, and fish can be bought/sold. These core doings - or sub-assemblages or capacities - maintain the assemblage. Each (sub)assemblage reflects different actualisations of constellations of relations and elements (buyers, fishers, fuel, permits, vessels and wind). The RBM thereby reflects an artificial fishery in which agents (elements) and their links (relations) engage in (enabling) fishing and buying/selling.

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.

This is a NetLogo version of Buhl et al.’s (2005) model of self-organised digging activity in ant colonies. It was built for a master’s course on self-organisation and its intended use is still educational. The ants’ behavior can easily be changed by toggling switches on the interface, or, for more advanced students, there is R code included allowing the model to be run and analysed through RNetLogo.

This model simulates how collective self-organisation among individuals that manage irrigation resource collectively.