An agent-based model of urban travel behaviour in Dublin, Ireland, built in NetLogo and empirically grounded in 2016 travel survey data. Each agent represents a Dublin resident initialised with real socio-demographic attributes — including age, gender, household size and car ownership, income, driving licence status, and access to local amenities — alongside observed trip characteristics such as distance, travel time, and trip type (work, shopping, leisure).
At each time step, agents choose between four transport modes (car, public transport, cycling, and walking) across short, medium, and long trips. Mode choice is governed by a preference vector that weighs personal need satisfaction against social influence from neighbouring agents reflecting consumat framework. Satisfaction evolves dynamically based on cost (incorporating Irish motor tax bands and per-km operating rates), travel time, and trip-type suitability, with an uncertainty parameter capturing variability in perceived utility over time.
The model tracks aggregate modal shares and total CO2 emission at each tick, enabling exploration of how policy interventions — such as fuel taxation, public transport pricing, or active travel incentives — might shift the city’s travel demand profile over 100 simulated days.

Protein 2.0 is a systems model of the Norwegian protein sector designed to explore the potential impacts of carbon taxation and the emergence of cultivated meat and dairy technologies. The model simulates production, pricing, and consumption dynamics across conventional and cultivated protein sources, accounting for emissions intensity, technological learning, economies of scale, and agent behaviour. It assesses how carbon pricing could alter the competitiveness of conventional beef, lamb, pork, chicken, milk, and egg production relative to emerging cultivated alternatives, and evaluates the implications for domestic production, emissions, and food system resilience. The model provides a flexible platform for exploring policy scenarios and transition pathways in protein supply. Further details can be found in the associated publication.

For deep decarbonisation, the design of climate policy needs to account for consumption choices being influenced not only by pricing but also by social learning. This involves changes that pertain to the whole spectrum of consumption, possibly involving shifts in lifestyles. In this regard, it is crucial to consider not just short-term social learning processes but also slower, longer-term, cultural change. Against this background, we analyse the interaction between climate policy and cultural change, focusing on carbon taxation. We extend the notion of “social multiplier” of environmental policy derived in an earlier study to the context of multiple consumer needs while allowing for behavioural spillovers between these, giving rise to a “cultural multiplier”. We develop a model to assess how this cultural multiplier contributes to the effectiveness of carbon taxation. Our results show that the cultural multiplier stimulates greater low-carbon consumption compared to fixed preferences. The model results are of particular relevance for policy acceptance due to the cultural multiplier being most effective at low-carbon tax values, relative to a counter-case of short-term social interactions. Notably, at high carbon tax levels, the distinction between social and cultural multiplier effects diminishes, as the strong price signal drives even resistant individuals toward low-carbon consumption. By varying socio-economic conditions, such as substitutability between low- and high-carbon goods, social network structure, proximity of like-minded individuals and the richness of consumption lifestyles, the model provides insight into how cultural change can be leveraged to induce maximum effectiveness of climate policy.

Country-by-Country Reporting and Automatic Exchange of Information have recently been implemented in European Union (EU) countries. These international tax reforms increase tax compliance in the short term. In the long run, however, taxpayers will continue looking abroad to avoid taxation and, countries, looking for additional revenues, will provide opportunities. As a result, tax competition intensifies and the initial increase in compliance could reverse. To avoid international tax reforms being counteracted by tax competition, this paper suggests bilateral responsive regulation to maximize compliance. This implies that countries would use different tax policy instruments toward other countries, including tax and secrecy havens.

To assess the effectiveness of fully or partially enforce tax policies, this agent based model has been ran many times under different enforcement rules, which influence the perceived enforced- and voluntary compliance, as the slippery-slope model prescribes. Based on the dynamics of this perception and the extent to which agents influence each other, the annual amounts of tax evasion, tax avoidance and taxes paid are calculated over longer periods of time.

The agent-based simulation finds that a differentiated policy response could increase tax compliance by 6.54 percent, which translates into an annual increase of €105 billion in EU tax revenues on income, profits, and capital gains. Corporate income tax revenues in France, Spain, and the UK alone would already account for €35 billion.

An agent based simple economy model that examines the effect of taxation and almsgiving (particularly Islamic almsgiving - zakat) for ameliorating wealth inequality.