Graphical AbstractHighlights d An ABM is used to study the cultural evolution of sustainable behaviors d Behaviors emerge as a function of affordances, social learning, and habits d The affordances in an environment have a major effect on behavior adoption d The ABM is validated against cycling behaviors in Copenhagen Authors Roope Oskari Kaaronen, Nikita Strelkovskii Correspondence roope.kaaronen@helsinki.fi In Brief Kaaronen and Strelkovskii have designed an agent-based model to study the cultural evolution of sustainable behaviors. Behaviors emerge as a product of personal, environmental, and social factors. Particularly the structure of the environment has an effect on the adoption of pro-environmental behaviors. Even linear changes in pro-environmental affordances (action opportunities) can trigger non-linear collective behavior change. The model is validated against cycling behaviors in Copenhagen. This model gives further justification for policies and urban design that make proenvironmental behavior psychologically salient, accessible, and easy.

To reach sustainability transitions, we must learn to leverage social systems into tipping points, where societies exhibit positive feedback loops in the adoption of sustainable behavioural or cultural traits. For instance, future sustainability transitions are often portrayed as having roughly ‘S-shaped’ timelines, introducing pro-environmental lifestyles at an increasingly fast rate until a new sustainable state is reached. However, much less is known about the most efficient ways to reach such transitions, or how self-reinforcing systemic transformations might be instigated through means of policy.This research article models behaviour as the function of the person and their environment. We employ an agent-based model to study the emergence of social tipping points through five interconnected factors which have been previously identified to constitute an ecological approach to human behaviour. These are 1. affordances (action opportunities provided by the physical environment), 2. asocial learning and habituation, 3. personal states (such as intentions and habits), 4. cultural niche construction, and 5. social learning in a social network.Our model suggests that even a linear introduction of pro-environmental affordances to a social system can have non-linear self-reinforcing effects on the emergence of collective pro-environmental behaviour patterns. We also conduct empirical validation with a case study, validating the model against data of the evolution of cycling and driving behaviours in central Copenhagen. Our model gives further evidence and justification for policies that make pro-environmental behaviour psychologically salient, easy, and the path of least resistance.

The complexity, multidimensionality, and persistence of the COVID-19 pandemic have prompted both researchers and policymakers to turn to transdisciplinary methods in dealing with the wickedness of the crisis. While there are increasing calls to use systems thinking to address the intricacy of COVID-19, examples of practical applications of systems thinking are still scarce. We revealed and reviewed eight studies which developed causal loop diagrams (CLDs) to assess the impact of the COVID-19 pandemic on a broader socioeconomic system. We find that major drivers across all studies are the magnitude of the infection spread and government interventions to curb the pandemic, while the most impacted variables are public perception of the pandemic and the risk of infection. The reviewed COVID-19 CLDs consistently exhibit certain complexity patterns, for example, they contain a higher number of two- and three-element feedback loops than comparable random networks. However, they fall short in representing linear complexity such as multiple causes and effects, as well as cascading impacts. We also discuss good practices for creating and presenting CLDs using the reviewed diagrams as illustration. We suggest that increasing transparency and rigor of the CLD development processes can help to overcome the lack of systems thinking applications to address the challenges of the COVID-19 crisis.

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The ongoing COVID-19 crisis and measures aimed at curbing the pandemic have a widespread impact on various aspects of well-being, such as housing, social connections, and others. Moreover, COVID-19 does not affect all population groups equally. This study analyzes the impact of major COVID-19 non-pharmaceutical interventions (NPIs) on a set of national well-being indicators from the most recent version of the OECD Well-Being Framework. Using causal loop diagrams (systems maps), we consider direct and indirect effects of these policies on various components of the national well-being system. Our results show that business closures directly and/or indirectly impact more national well-being components than any other policy. The most affected national well-being components by all policies are life satisfaction, perceived health, and prevalence of depressive symptoms. In addition, we specify how the impact of the anti-pandemic measures differs for various population strata, using the degree of income and employment loss as key stratifying variables. Our insights can be helpful to identify and promote measures that can alleviate the adverse effects of the COVID-19 crisis on the national well-being.

Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general.