The Impact of Collective Risk on Social Norms and Cooperation

Post by Leanna Kalinowski

What's the science?

Collective action problems exist where groups benefit from cooperating to achieve a shared outcome, but personal incentives drive individuals to instead rely on others’ efforts. Examples of this can be seen in reducing infectious disease spread and climate change action among many other societal challenges. Laws to foster cooperation to address these global issues are often unavailable, unenforceable, or insufficient, leading society to rely on social norms to encourage compliance. However, it is not fully understood how social norms shape cooperation among strangers and whether the level of threat faced by a society plays a role in the norms that evolve. This week in Nature Communications, Szekely and colleagues used a 30-day collective-risk social dilemma to measure how social norms change in response to varying levels of risk.

How did they do it?

Participants first completed personality trait tests and a demographic questionnaire to determine individual-level factors that may lead an individual to follow social norms. Then, they were separated into groups of six and interacted through 28 daily rounds of the collective-risk social dilemma, with the groups being shuffled daily. At the beginning of each round, each participant was allocated 100 points and asked to decide how many of those points to contribute to the group’s collective pool. If a threshold number of points (300) was met, the collective risk was averted, and all participants got to keep their unspent points. If the threshold was not met, participants risked losing their points determined by a pre-set probability (p).

To determine whether higher risk environments led to stronger social norms, the probability of losing points was manipulated. Half of the participants experienced a low-risk environment for days 1-14 followed by a high-risk environment for days 15-28, while the other half of the participants experienced the risk environments in the opposite order. Following each round, participants’ personal normative beliefs and societal expectations were measured. Following the 28th round, participants were asked to determine their level of punishment for individuals who did not contribute at least 50 points.

What did they find?

First, the researchers found that societal expectations and personal normative beliefs have strong and positive associations with cooperative behaviors (i.e., number of points contributed). They then assessed whether cooperative behaviors are impacted by risk level, finding that there were stronger social norms in the high-risk environment compared to the low-risk environment. They also found that groups with stronger social norms are more likely to contribute more points and reach the collective threshold level compared to those with weaker social norms.

Next, they found that participants in the low-risk environment experienced a rapid strengthening of social norms upon entering the high-risk environment. Conversely, participants in the high-risk environment experienced a slow deterioration of social norms upon entering the low-risk environment. The presence of social norms was further indicated by punishment levels. Regardless of risk, low contributors (< 50 points) are punished with a higher intensity than high contributors (> 50 points).

What's the impact?

Taken together, these findings show that high risk of collective loss increases the strength of social norms, reduces tolerance of those who deviate from social norms, and increases cooperation. Understanding how social norms emerge during high-risk situations is imperative for developing policies to foster cooperation in the face of future global crises.

Szekely et al. Evidence from a long-term experiment that collective risks change social norms and promote cooperation. Nature Communications (2021). Access the original scientific publication here. 

The Impact of Social Learners on Collective Decision-Making

Post by Lani Cupo

What's the science?

In democratic societies, collective decisions, such as who should hold power, or what action should be taken to address climate change, can drastically impact society. But when people make decisions in groups, the most popular option is sometimes chosen even though it does not have the most merit. This phenomenon is due in part to the presence of those identified here as social learners who adopt the opinions of others instead of critically assessing options for themselves. This week in PNAS, Yang and colleagues developed a mathematical framework for investigating whether there is a critical threshold of social learners that can be present in a collective decision, after which one option may prevail because of popularity, rather than merit. 

How did they do it?

The authors created a dynamical system model which integrated two options (X and Y) with relative merit (m) associated with each option, where m was a number between zero and one. The model incorporated differing proportions of social and independent learners (s) in the population. Finally, it included one parameter as a function that is hypothesized to model two types of conformity, normative (engaging in a behavior because others do it), and informational (engaging in a behavior because it is the right thing to do). The authors derived transition rates between the options for the different types of learners, where social learners will transition between the options based on the popularity of the option, but independent learners transition based on the merit of the option. This allowed the authors to examine the fixed points of the equation, where the proportion of people favoring a given option stops changing. They also investigated how stable these fixed points are when the model is perturbed. Their conclusions remained the same when they changed the model to account for opinion on a spectrum from independent to social, when they only allowed individuals to be impacted only by their local environment, and when they introduced statistical noise to the model. Finally, the researchers simulated a model incorporating the strength of opinion weighting towards option X or Y. 

What did they find?

When X and Y are options with equal merit, there is a critical threshold for the proportion of social learners after which the model bifurcates into two branches, implying either option X or Y could be selected. In the case that the model is not equal between groups, the majority will favor the more meritorious option up to a critical point, but if the proportion of social learners is too high, instability is introduced into the model, meaning there can be cases in collective decision making where the less meritorious option is still chosen. The critical threshold is determined both by the discrepancy in merit between the two options and the conformity function. The threshold that these parameters identify predicts the threshold above which the proportion of social learners harms the decision. Notably, the model is flexible to adapt to different behaviors modelled through the conformity function or to allow parameters, such as the strength of opinions, meaning it represents a flexible tool that can be used to model different situations. 

What's the impact?

This study investigated the impact of social learners on collective decision-making, demonstrating there is a threshold above which social learners may negatively impact the outcome of collective decisions. The outcome of collective decisions can drastically impact daily life—not only in small communities but on a national and global scale as well. The mathematical framework presented provides future studies with the ability to examine social learning in varied and complex scenarios. 

Yan et al. Dynamical system model predicts when social learners impair collective performance. PNAS (2021). Access the original scientific publication here

Decision Processes Leading to Unhealthy Food Choices

Post by Andrew Vo

What's the science?

After making poor dietary choices, we often blame our actions on either a strong preference for tasty (but oftentimes unhealthy) food, or on poor self-control. Traditional computational models characterize such value-based decisions as a dynamic accumulation of evidence that biases us towards one option over another. These models, however, do not account for distinct contributions of separable attributes to a decision (e.g., how health and taste attributes are integrated with different weights and at different times in evidence accumulation). This week in Nature Human Behaviour, Sullivan and Huettel use an updated computational framework to better understand how distinct attributes influence decision processes that could lead to unhealthy food choices.

How did they do it?

The authors recruited a group of young adults who arrived hungry at the lab after a four-hour fast. They were then asked to rate 30 different snack foods based on tastiness, healthiness, and ‘wanting’ attributes. Before beginning the main task, participants received a behavioral primer that emphasized the importance of either healthy or tasty choices. During a main, binary choice task, they were presented with pairs of food items (that they had previously rated) and were asked to indicate which they would like to eat more. Of the 300 self-paced trials, half were designed to be “conflict trials” in which one option was tastier but less healthy than the other, whereas the other half were non-conflict trials in which both options were closely matched.

Participants’ food choices and response times (RTs) were fitted using a multi-attribute, time-dependent, drift diffusion model (mtDDM) (a statistical model). This model has the advantage of distinguishing the various contributions of different attributes to a decision. To do this, it estimates (1) drift slope, which captures the rate of evidence accumulation for each attribute, and (2) drift latency, which describes when each attribute begins to exert its influence during evidence accumulation.

What did they find?

The authors found faster RTs for conflict versus non-conflict trials, as participants made fast unhealthy choices over healthier ones. Those participants who were primed with health information were found to put less weight on taste information, which marginally increased their likelihood of making healthy choices.

The mtDDM estimated that taste drift slopes were larger (steeper) than health drift slopes and taste drift latencies were earlier than health drift latencies. These results suggest that bias towards tasty versus healthy food choices is due to a greater weighting and earlier entry of taste information into evidence accumulation. To test whether slope and latency independently influenced food choices, multiple linear regressions of drift slope and latency differences (i.e., taste minus health) were performed. Both drift slopes and latencies predicted individual differences in the likelihood of healthy food choices. Finally, examining the relationship between trial-by-trial RTs and healthy choices in conflict trials, the authors found that longer RTs were associated with healthier food choices. This suggests that longer RTs allow time for slower-processed healthy information to influence evidence accumulation.

What's the impact?

This study demonstrates how the influence of different attributes on decision-making processes might explain our food choices. The results provide insight into how we can augment our thinking to make better decisions for our long-term benefit, such as considering the healthiness alongside the tastiness of a food item or taking more time to seek out health information on a food choice. Understanding the timing of decision processes in the brain might also be key to creating effective interventions that help people make better choices — not just in terms of diet but also in financial decisions, for example. Much like how you should look before you leap, consider pausing before you place that next restaurant order.

Sullivan & Huettel. Healthful choices depend on the latency and rate of information accumulation. Nature Human Behaviour (2021). Access the original scientific publication here.