In the last decade, a number of studies in the behavioural sciences, particularly in psychology and economics, have explored the complexity of individual risk behaviour and its underlying factors. Most previous studies have examined the influences of various socio-economic, cognitive, biological and psychological factors on human decision-making however, the relationship between the decision-makers’ risk preferences and occupational background has not received much empirical attention. Accordingly, in the current study, we investigated how occupational background, together with decision-making framing (e.g., variations in (...) decision domain, context, presentation of risk and utility ratios), influence participants’ risk preferences for decision options with equivalent expected utility. Our novel findings indicate that risk preferences may vary among individuals from different occupational backgrounds. As such, when the task was framed in gain terms, participants who mostly deal with health/safety-related risks on a day-to-day basis (high-risk occupations) were predominantly risk-averse (avoiding risky options), while participants who mostly deal with financial/social risks (white-collar occupations) were prone to risk-seeking behaviour (avoiding certain options). Specifically, in “high-risk” occupations, participants’ pattern of choices changed from risk-averse in gain scenarios to risk-seeking in loss scenarios. However, the opposite pattern of risk preferences was found in participants with “white-collar” occupations. Our findings indicate that decision-makers’ occupational backgrounds influence risk preferences under some circumstances. (shrink)

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping. Events in a time series can be experienced sequentially, or they can also be retrospectively viewed simultaneously, not experienced individually in real time. In one experiment, we studied the influence of presentation (...) mode on two sorts of judgment: predictions of the next event and estimation of the average value of all the events in the presented series. Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model—the adaptive anchoring model —to account for the difference between processing sequences of dynamically and statically presented stimuli. ADAM captures how variation in presentation mode produces variation in responses in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. (shrink)