The climate commons dilemma: how can humanity solve the commons dilemma for the global climate commons?

In the era when human activities can fundamentally alter the planetary climate system, a stable climate is a global commons. However, the need to develop the economy to sustain the growing human population poses the Climate Commons Dilemma. Although citizens may need to support policies that forgo their country's economic growth, they may instead be motivated to grow their economy while freeriding on others' efforts to mitigate the ongoing climate change. To examine how to resolve the climate commons dilemma, we constructed a Climate Commons Game (CCG), an experimental analogue of the climate commons dilemma that embeds a simple model of the effects of economic activities on global temperature rise and its eventual adverse effects on the economy. The game includes multiple economic units, and each participant is tasked to manage one economic unit while keeping global temperature rise to a sustainable level. In two experiments, we show that people can manage the climate system and their economies better when they regarded the goal of environmentally sustainable economic growth as a singular global goal that all economic units collectively pursue rather than a goal to be achieved by each unit individually. In addition, beliefs that everyone shares the knowledge about the climate system help the group coordinate their economic activities better to mitigate global warming in the CCG. However, we also found that the resolution of the climate commons dilemma came at the cost of exacerbating inequality among the economic units in the current constrains of the CCG. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10584-021-02989-2.

Modeling continuous outcome color decisions with the circular diffusion model: Metric and categorical properties.

The circular diffusion model is extended to provide a theory of the speed and accuracy of continuous outcome color decisions and used to characterize eye-movement decisions about the hues of noisy color patches in an isoluminant, equidiscriminability color space. Heavy-tailed distributions of decision outcomes were found with high levels of chromatic noise, similar to those found in visual working memory studies with high memory loads. Decision times were longer for less accurate decisions, in agreement with the slow error property typically found in difficult 2-choice tasks. Decision times were shorter, and responses were more accurate in parts of the space corresponding to nameable color categories, although the number and locations of the categories varied among participants. We show that these findings can be predicted by a theory of across-trial variability in the quality of the evidence entering the decision process, represented mathematically by the drift rate of the diffusion process. The heavy-tailed distributions of decision outcomes and the slow-error pattern can be predicted by either of 2 models of drift rate. One model is based on encoding failures and the other is based on a nonlinear transformation of the stimulus space. Both models predict highly inaccurate stimulus representations on some trials, leading to heavy-tailed distributions and slow errors. The color-category effects were successfully modeled as stimulus biases in a similarity-choice framework, in which the drift rate is the vector sum of the encoded metric and categorical representations of the stimulus. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Correction: Causal knowledge promotes behavioral self-regulation: An example using climate change dynamics.

[This corrects the article DOI: 10.1371/journal.pone.0184480.].

Causal knowledge promotes behavioral self-regulation: An example using climate change dynamics.

Adopting successful climate change mitigation policies requires the public to choose how to balance the sometimes competing goals of managing CO2 emissions and achieving economic growth. It follows that collective action on climate change depends on members of the public to be knowledgeable of the causes and economic ramifications of climate change. The existing literature, however, shows that people often struggle to correctly reason about the fundamental accumulation dynamics that drive climate change. Previous research has focused on using analogy to improve people's reasoning about accumulation, which has been met with some success. However, these existing studies have neglected the role economic factors might play in shaping people's decisions in relation to climate change. Here, we introduce a novel iterated decision task in which people attempt to achieve a specific economic goal by interacting with a causal dynamic system in which human economic activities, CO2 emissions, and warming are all causally interrelated. We show that when the causal links between these factors are highlighted, people's ability to achieve the economic goal of the task is enhanced in a way that approaches optimal responding, and avoids dangerous levels of warming.

Dilemma of dilemmas: how collective and individual perspectives can clarify the size dilemma in voluntary linear public goods dilemmas.

Empirical findings on public goods dilemmas indicate an unresolved dilemma: that increasing size-the number of people in the dilemma-sometimes increases, decreases, or does not influence cooperation. We clarify this dilemma by first classifying public goods dilemma properties that specify individual outcomes as individual properties (e.g., Marginal Per Capita Return) and group outcomes as group properties (e.g., public good multiplier), mathematically showing how only one set of properties can remain constant as the dilemma size increases. Underpinning decision-making regarding individual and group properties, we propose that individuals are motivated by both individual and group preferences based on a theory of collective rationality. We use Van Lange's integrated model of social value orientations to operationalize these preferences as an amalgamation of outcomes for self, outcomes for others, and equality of outcomes. Based on this model, we then predict how the public good's benefit and size, combined with controlling individual versus group properties, produce different levels of cooperation in public goods dilemmas. A two (low vs. high benefit) by three (2-person baseline vs. 5-person holding constant individual properties vs. 5-person holding constant group properties) factorial experiment (group n = 99; participant n = 390) confirms our hypotheses. The results indicate that when holding constant group properties, size decreases cooperation. Yet when holding constant individual properties, size increases cooperation when benefit is low and does not affect cooperation when benefit is high. Using agent-based simulations of individual and group preferences vis-à-vis the integrative model, we fit a weighted simulation model to the empirical data. This fitted model is sufficient to reproduce the empirical results, but only when both individual (self-interest) and group (other-interest and equality) preference are included. Our research contributes to understanding how people's motivations and behaviors within public goods dilemmas interact with the properties of the dilemma to lead to collective outcomes.