This week, we had Wolfgang Gaissmaier from the University of Konstanz give a talk in the Social, Economic, and Decision Psychology Colloquium. Wolfgang talked about how lay people and experts (e.g., physicians) deal with risk information and ways to improve risk literacy in the population at large.

Risk Literacy

“In this world, nothing is certain except death and taxes,” Benjamin Franklin noted in 1789. Yet living with uncertainty can be extremely difficult, especially when our own health is at stake. Risk literacy refers to the ability to deal with risks in an informed way, which requires an understanding of statistics. However, both the general public as well as experts (including politicians and physicians) often lack such an  understanding and draw wrong conclusions, which became apparent in various recent crises – from BSE to EHEC – and can become a safety risk itself. In the 1930s, science fiction author H. G. Wells predicted that for an educated citizenship in a modern democracy, statistical thinking would be as indispensable as reading and writing. At the beginning of the 21st century, nearly everyone in industrial societies has been taught reading and writing, but not statistical thinking. Major preconditions for risk literacy are transparent risk communication and teaching statistical thinking. Understanding risks can also shape the emotional climate in a society so that hopes and anxieties are no longer as easily manipulated from outside and citizens can develop a better informed and more relaxed attitude towards health.

This week, we had Eric Shulz visiting from UCL and giving the following talk:

Active learning as a means to distinguish among prominent decision strategies

There has been a long-standing debate about whether people rely on single cues for decision making or use more integrative strategies that weigh and add information. A key factor for determining which decision strategy is adapted is the degree to which a learning environment is compensatory. In the current study, we propose a new way to tell apart one-reason decision making (Take-The-Best) and more integrative decision mechanisms (Regression) by the means of active learning. We argue that if a cognitive agent has learned to obey a specific decision making procedure in a pre-defined environment, then the way she selects information over time should reflect that very same architecture. Based on an entropy minimizing active learning algorithm we set out to test this assumption. In an active learning experiment we introduce environments of varying “compensatoriness” by a stick breaking process and let both models and humans actively learn. We then compare queries of a rank and weight-based learning algorithm with participants active queries. Results show that people seem to follow a rank-based learning strategy in non-compensatory environments, and -surprisingly – prefer weight-based, certain queries in compensatory environments.