Schulze, C. & Pachur, T. (2019). Going round in circles: How social structures guide and limit search. In R. Hertwig, T. Pleskac, T. Pachur, & the Center for Adaptive Rationality (Eds.), Taming uncertainty (pp. xx–xx). Boston, MA: MIT Press. doi:XXXXXXX

This is the interactive element for Chapter 4. This element allows you to visualize the diminishing returns in inferential accuracy from sampling more social information under different environmental conditions described in the chapter. You can change two important properties of the environment. The first one is the skewness of the frequency distribution across events. Many environmental quantities follow highly skewed distributions in which a select few objects dominate the others (e.g., the distribution of personal wealth in a population). The second environmental property you can change is the spatial clustering of events. People tend to interact and form social ties with others who have similar sociodemographic, behavioral, and attitudinal characteristics—a phenomenon known as homophily.

In the figure below, you will then see how these environmental properties affect the accuracy-gain of drawing an increasing number of social samples. The figure shows the accuracy in inferring which of two events is more frequent as a function of the number of randomly drawn samples. Also visualized are the level of skewness and clustering you selected for the simulation.

Frequency distribution

Spatial distribution


AccuracySample size

In many critical aspects of everyday life, no objective social statistics are available. Taking a mental poll of the people one knows personally is an effective strategy for reducing this uncertainty and forming approximate representations of the world’s social texture. Natural structures in a person’s social network can guide and limit this search for relevant information in social memory.

The goal of this task was to show you that it is not inevitable that limited search for instances in memory comes at the price of very low accuracy. You saw that, under conditions that are arguably common in natural environments—when the frequency distribution of the events is skewed and the events occur in spatial clusters—heuristic sampling that starts with a very small sample and sequentially expands search only if that sample does not yield sufficient evidence represents a useful tool for judging event frequencies. Because conditional and ordered sampling potentially ignores redundant information in later social circles, it can achieve higher accuracy than can unconditional random draws of samples of the same size.

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