Identifying different choice mechanisms in economic decision-making: methodological and empirical insights

Identifying what mechanisms are exactly used by individuals in economic decisions is one core objective of different disciplinary fields, ranging from economics, psychology, cognitive science, and neuroscience. Although significant progress has been made among the different fields over the last decades, the identification of different choice mechanisms is not trivial, and research still faces a number of challenges, such as inappropriate reverse inferences (Krajbich et al., 2015). In the current thesis, I provide insights into the understanding and measurement of economic choice mechanisms. My research questions are based on the analysis of data properties that are specific to a mixture of different strategies and are not susceptible to reverse inference problems, as well as the development of carefully designed experimental tasks and setups to mitigate those problems. In Chapter 2 and 3, specifically, I tackle the application of the fixed-point property (FP) and its ability to identify the presence of a mixture of cognitive processes in experimental data. The FP is a particularly useful property of mixture distributions (Falmagne, 1968). Consider an experiment where two processes jointly account for the final behavior (e.g., RTs) and the relative contribution—i.e., mixture proportion— of each process for the final behavior changes, depending on the experimental conditions. The FP entails that, under such experiment, the observed distributions of a dependent variable for the different experimental conditions all cross at the same point (Falmagne, 1968). Leveraging this method, some researchers have already identified mixture distributions in their experimental data (Couto et al., 2020; Grange, 2016; Maanen et al., 2014; Poboka et al., 2014). However, some performance metrics are still missing for researchers aiming to apply the FP. In Chapter 2 and 3, I provide such performance metrics. In Chapter 4 and 5, I systematically investigate and dissociate different choice mechanisms with the use of carefully designed experimental tasks and setups. To investigate multiple choice mechanisms experimentally, researchers have leveraged the presumed difference in processing speed between different types of processes and assumed that slow(er) decisions represent one type of process and fast(er) decisions other type (Rubinstein, 2007). Likewise, researchers have used time pressure as a manipulation to make inferences about the nature of the processes (Kocher et al., 2018). This reasoning has been applied in behavioral economics to investigate whether individuals are naturally inclined to be e.g., risk-averse or risk-seeking under time pressure (Kirchler et al., 2017; Kocher & Sutter, 2006). However, the effects of time pressure are still inconsistent between studies (Kocher et al., 2018), showing that there is not always a correlation between measured RTs (i.e., faster decisions) and risk behavior (i.e., safer decisions). It is therefore crucial that multiple choice mechanisms are investigated and interpreted comparatively and relative to specific experimental factors (i.e., in addition to time pressure) and/or through the analysis of both behavioral and neural signatures (i.e., in addition to measured RTs and/or risk behavior). The research I conduct in Chapter 4 and 5 is based on these research objectives.