Using exploratory modeling to challenge narratives of risk governance in Mexico City.

Achieving more sustainable adaptation to social-environmental change demands the transformation of the narratives that provide the rationale for risk governance. These narratives often reflect long-standing beliefs about social and political relationships, ascribe actions and responsibilities, and specify solutions to risk. When such solutions are implemented through material investments in landscapes, these narratives become embedded in physical infrastructure with long legacies. Dominant narratives can mask a range of divergent problem framings. By masking alternatives, narratives can contribute to the persistence of unsustainable governance trajectories. Decision-support tools have begun to represent narratives as drivers of system dynamics; making narratives visible can reveal opportunities for more sustainable governance. We present the results of the project "The Dynamics of Multi-Scalar Adaptation in the Megalopolis", a dynamic, exploratory model of socio-hydrological risks in Mexico City that was designed to both endogenize and simultaneously challenge the dominant narratives that characterize water-risk governance in the city. Qualitative data characterize dominant narratives at city and borough scales. An agent-based model, informed by multicriteria decision analysis and coupled with hydrological, urbanization, and climatic model inputs, permitted the development of exploratory governance scenarios designed to challenge dominant narratives. Scenarios revealed how dominant narratives may contribute to the persistence of vulnerability "hotspots" in the city, despite stated goals of equity and vulnerability alleviation. Participatory workshops with representatives of the city government illustrate how making such narratives visible through exploratory modeling can lead to a questioning of prior assumptions and causal relations, recognition of a need for intersectoral collaboration, and insights into potential management strategies.

Knowledge infrastructure and safe operating spaces in social-ecological systems.

Maintaining safe operating spaces for exploited natural systems in the face of uncertainty is a key sustainability challenge. This challenge can be viewed as a problem in which human society must navigate in a limited space of acceptable futures in which humans enjoy sufficient well-being and avoid crossing planetary boundaries. A critical obstacle is the nature of society as a controller with endogenous dynamics affected by knowledge, values, and decision-making fallacies. We outline an approach for analyzing the role of knowledge infrastructure in maintaining safe operating spaces. Using a classic natural resource problem as an illustration, we find that a small safe operating space exists that is insensitive to the type of policy implementation, while in general, a larger safe operating space exists which is dependent on the implementation of the "right" policy. Our analysis suggests the importance of considering societal response dynamics to varying policy instruments in defining the shape of safe operating spaces.

Job Mobility and Wealth Inequality.

The extent to which employees change jobs, known as the job mobility rate, has been steadily declining in the US for decades. This decline is understood to have a negative impact on both productivity and wages, and econometric studies fail to support any single cause brought forward. This decline coincides with decreases in household savings, increases in household debt and wage stagnation. We propose that the decline could be the consequence of a complex interaction between mobility, savings, wages and debt, such that if changing jobs incurs costs which are paid out of savings, or incurs debt in the absence of sufficient savings, a negative feedback loop is generated. People are further restricted in making moves by their debt obligations and inability to save, which in turn depresses wages further. To explore this hypothesis, we developed a stylized model in which agents chose their employment situation based on their opportunities and preferences for work and where there are costs to changing jobs and the possibility of borrowing to meet those costs. We indeed found evidence of a negative feedback loop involving changes, wages, savings and debt, as well as evidence that this dynamic results in a level of wealth inequality on the same scale as we see today in the US.

Fragility of the provision of local public goods to private and collective risks.

Smallholder agricultural systems, strongly dependent on water resources and investments in shared infrastructure, make a significant contribution to food security in developing countries. These communities are being increasingly integrated into the global economy and are exposed to new global climate-related risks that may affect their willingness to cooperate in community-level collective action problems. We performed field experiments on public goods with private and collective risks in 118 small-scale rice-producing communities in four countries. Our results indicate that increasing the integration of those communities with the broader economic system is associated with lower investments in public goods when facing collective risks. These findings indicate that local public good provision may be negatively affected by collective risks, especially in communities more integrated with the market economy.

On code sharing and model documentation of published individual and agent-based models.

Being able to replicate research results is the hallmark of science. Replication of research findings using computational models should, in principle, be possible. In this manuscript, we assess code sharing and model documentation practices of 7500 publications about individual-based and agent-based models. The code availability increased over the years, up to 18% in 2018. Model documentation does not include all the elements that could improve the transparency of the models, such as mathematical equations, flow charts, and pseudocode. We find that articles with equations and flow charts being cited more among other model papers, probably because the model documentation is more transparent. The practices of code sharing improve slowly over time, partly due to the emergence of more public repositories and archives, and code availability requirements by journals and sponsors. However, a significant change in norms and habits need to happen before computational modeling becomes a reproducible science.

Operationalizing the feedback between institutional decision-making, socio-political infrastructure, and environmental risk in urban vulnerability analysis.

Urban adaptation to climate change is likely to emerge from the responses of residents, authorities, and infrastructure providers to the impact of flooding, water scarcity, and other climate-related hazards. These responses are, in part, modulated by political relationships under cultural norms that dominate the institutional and collective decisions of public and private actors. The legacy of these decisions, which are often associated with investment in hard and soft infrastructure, has lasting consequences that influence current and future vulnerabilities. Making those decisions visible, and tractable is, therefore, an urgent research and political challenge in vulnerability assessments. In this work, we present a modeling framework to explore scenarios of institutional decision-making and socio-political processes and the resultant effects on spatial patterns of vulnerability. The approach entails using multi-criteria decision analysis, agent-based models, and geographic information simulation. The approach allows for the exploration of uncertainties, spatial patterns, thresholds, and the sensitivities of vulnerability outcomes to different policy scenarios. Here, we present the operationalization of the framework through an intentionally simplified model example of the governance of water in Mexico City. We discuss results from this example as part of a larger effort to empirically implement the framework to explore sociohydrological risk patterns and trade-offs of vulnerability in real urban landscapes.

Managing household socio-hydrological risk in Mexico city: A game to communicate and validate computational modeling with stakeholders.

Residents of Mexico City experience major hydrological risks, including flooding events and insufficient potable water access for many households. A participatory modeling project, MEGADAPT, examines hydrological risk as co-constructed by both biophysical and social factors and aims to explore alternative scenarios of governance. Within the model, neighborhoods are represented as agents that take actions to reduce their sensitivity to exposure and risk. These risk management actions (to protect their households against flooding and scarcity) are based upon insights derived from focus group discussions within various neighborhoods. We developed a role-playing game based on the model's rules in order to validate the assumptions we made about residents' decision-making given that we had translated qualitative information from focus group sessions into a quantitative model algorithm. This enables us to qualitatively validate the perspective and experience of residents in an agent-based model mid-way through the modeling process. Within the context of described hydrological events and the causes of these events, residents took on the role of themselves in the game and were asked to make decisions about how to protect their households against scarcity and flooding. After the game, we facilitated a discussion with residents about whether or not the game was realistic and how it could be improved. The game helped to validate our assumptions, validate the model with community members, and reinforced our connection with the community. We then discuss the potential further development of the game as a learning and communication tool.

Learning from regulatory failure: How Ostrom's restorative justice design principle helps naïve groups create wiser enforcement systems to overcome the tragedy of the commons.

Rule enforcement is critical in democratic, self-governing societies. Many political disputes occur when citizens do not understand the fundamental rationales for enforcement (e.g., COVID-19 pandemic). We examined how naïve groups learn and develop wise enforcement systems. Based on theories from behavioral economics, political science, psychology, and education, we predicted that groups need to experience failure of an enforcement system, but be guided on restorative justice principles to collectively learn from this failure. Undergraduate students (N = 288) from a Midwestern U.S. metropolitan university self-governed a simulated common-pool resource with real financial payoffs. Groups began with one of three conditions designed to create different experiences with enforcement and regulatory failure: (a) no enforcement (no communication or peer sanctioning), (b) lax enforcement (communication with peer-sanctioning), or (c) regulatory abuse (peer sanctioning without communication). Half then received facilitated guidance on restorative justice principles (e.g., discuss whether/why to use sanctions). To examine cooperation, we measured how well participants maintained the resource. To examine group learning, we created a novel coding system, which tracked groups' constitutional decisions about conservation agreements and enforcement, conceptual understanding, and the enforcement systems they created. The no-enforcement and lax-enforcement conditions quickly yielded moderate cooperation via voluntary agreements. However, such agreements prevented groups from discovering how and why to use enforcement (peer sanctioning) to improve performance. Initial exposure to regulatory failure had different effects depending on facilitation. Unfacilitated groups fixated on initial misconceptions, causing them to abandon or create less sophisticated enforcement systems, hindering cooperation. Facilitated groups learned from prior failure-discovering principles of wise enforcement (e.g., collective efficiency, self-restraint)-and created more sophisticated enforcement systems (e.g., coordinated sanctions) that improved cooperation. Guidance on restorative justice principles and experience with regulatory abuse may be necessary preconditions for naïve individuals to understand and develop wiser collective enforcement systems.

Collective action within an environment of unknown unknowns: Experiences with the port of Mars Game.

There is limited research about how groups solve collective action problems in uncertain environments, especially if groups are confronted with unknown unknowns. We aim to develop a more comprehensive view of the characteristics that allow both groups and individuals to navigate such issues more effectively. In this article, we present the results of a new online experiment where individuals make decisions of whether to contribute to the group or pursue self-interest in an environment with high uncertainty, including unknown unknowns. The behavioral game, Port of Mars is framed as a first-generation habitat on Mars where participants have to make decisions on how much to invest in the shared infrastructure to maintain system health and how much to invest in personal goals. Participants can chat during the game, and take surveys before and after the game in order to measure personality attributes and observations from the game. Initial results suggest that a higher average social value orientation and more communication are the key factors that explain why some groups are more successful than others in surviving Port of Mars. Neither other attributes of players nor the group's communication content explain the observed differences between groups.

Joint effects of voluntary participation and group selection on the evolution of altruistic punishment.

It is puzzling how altruistic punishment of defectors can evolve in large groups of nonrelatives, since punishers should voluntarily bear individual costs of punishing to benefit those who do not pay the costs. Although two distinct mechanisms have been proposed to explain the puzzle, namely voluntary participation and group-level competition and selection, insights into their joint effects have been less clear. Here we investigated what could be combined effects of these two mechanisms on the evolution of altruistic punishment and how these effects can vary with nonparticipants' individual payoff and group size. We modelled altruistic punishers as those who contribute to a public good and impose a fine on each defector, i.e., they are neither pure punishers nor excluders. Our simulation results show that voluntary participation has negative effects on the evolution of cooperation in small groups regardless of nonparticipants' payoffs, while in large groups it has positive effects within only a limited range of nonparticipants' payoff. We discuss that such asymmetric effects could be explained by evolutionary forces emerging from voluntary participation. Lastly, we suggest that insights from social science disciplines studying the exit option could enrich voluntary participation models.

Exploring non-linear transition pathways in social-ecological systems.

Tipping point dynamics are fundamental drivers for sustainable transition pathways of social-ecological systems (SES). Current research predominantly analyzes how crossing tipping points causes regime shifts, however, the analysis of potential transition pathways from these social and ecological tipping points is often overlooked. In this paper, we analyze transition pathways and the potential outcomes that these may lead to via a stylized model of a system composed of interacting agents exploiting resources and, by extension, the overall ecosystem. Interactions between the social and the ecological system are based on a perception-exploitation framework. We show that the presence of tipping points in SES may yield counter-intuitive social-ecological transition pathways. For example, the high perception of an alarming ecological state among agents can provide short-term ecological benefits, but can be less effective in the long term, compared to a low-perception condition. This work also highlights how understanding non-linear interactions is critical for defining suitable transition pathways of any SES.

Evolution of cooperation and altruistic punishment when retaliation is possible.

Altruistic punishment is suggested to explain observed high levels of cooperation among non-kin related humans. However, laboratory experiments as well as ethnographic evidence suggest that people might retaliate if being punished, and that this reduces the level of cooperation. Building on existing models on the evolution of cooperation and altruistic punishment, we explore the consequences of the option of retaliation. We find that cooperation and altruistic punishment does not evolve with larger population levels if the option of retaliation is included.

On our rapidly shrinking capacity to comply with the planetary boundaries on climate change.

The planetary boundary framework constitutes an opportunity for decision makers to define climate policy through the lens of adaptive governance. Here, we use the DICE model to analyze the set of adaptive climate policies that comply with the two planetary boundaries related to climate change: (1) staying below a CO2 concentration of 550 ppm until 2100 and (2) returning to 350 ppm in 2100. Our results enable decision makers to assess the following milestones: (1) a minimum of 33% reduction of CO2 emissions by 2055 in order to stay below 550 ppm by 2100 (this milestone goes up to 46% in the case of delayed policies); and (2) carbon neutrality and the effective implementation of innovative geoengineering technologies (10% negative emissions) before 2060 in order to return to 350 ppm in 2100, under the assumption of getting out of the baseline scenario without delay. Finally, we emphasize the need to use adaptive path-based approach instead of single point target for climate policy design.

Computational models of collective behavior.

Computational models of human collective behavior offer promise in providing quantitative and empirically verifiable accounts of how individual decisions lead to the emergence of group-level organizations. Agent-based models (ABMs) describe interactions among individual agents and their environment, and provide a process-oriented alternative to descriptive mathematical models. Recent ABMs provide compelling accounts of group pattern formation, contagion and cooperation, and can be used to predict, manipulate and improve upon collective behavior. ABMs overcome an assumption that underlies much of cognitive science--that the individual is the crucial unit of cognition. The alternative advocated here is that individuals participate in collective organizations that they might not understand or even perceive, and that these organizations affect and are affected by individual behavior.

Pastoralists' responses to variation of rangeland resources in time and space.

We explore the response of pastoralists to rangeland resource variation in time and space, focusing on regions where high variation makes it unlikely that an economically viable herd can be maintained on a single management unit. In such regions, the need to move stock to find forage in at least some years has led to the evolution of nomadism and transhumance, and reciprocal grazing agreements among the holders of common-property rangeland. The role of such informal institutions in buffering resource variation is well documented in some Asian and African rangelands, but in societies with formally established private-property regimes, where we focus, such institutions have received little attention. We examine agistment networks, which play an important role in buffering resource variation in modern-day Australia. Agistment is a commercial arrangement between pastoralists who have less forage than they believe they require and pastoralists who believe they have more. Agistment facilitates the movement of livestock via a network based largely on trust. We are concerned exclusively with the link between the characteristics of biophysical variation and human aspects of agistment networks, and we developed a model to test the hypothesis that such a link could exist. Our model builds on game theory literature, which explains cooperation between strangers based on the ability of players to learn whom they can trust. Our game is played on a highly stylized landscape that allows us to control and isolate the degree of spatial variation and spatial covariation. We found that agistment networks are more effective where spatial variation in resource availability is high, and generally more effective when spatial covariation is low. Policy design that seeks to work with existing social networks in rangelands has potential, but this potential varies depending on localized characteristics of the biophysical variability.

Correction: The Role of Diverse Strategies in Sustainable Knowledge Production.

[This corrects the article DOI: 10.1371/journal.pone.0149151.].

The Role of Diverse Strategies in Sustainable Knowledge Production.

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.