Showing papers by "Klaus Fiedler published in 2015"

Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures.

Abstract: Many decisions in the lives of animals and humans require a fine balance between the exploration of different options and the exploitation of their rewards. Do you buy the advertised car, or do you test drive different models? Do you continue feeding from the current patch of flowers, or do you fly off to another one? Do you marry your current partner, or try your luck with someone else? The balance required in these situations is commonly referred to as the exploration– exploitation tradeoff. It features prominently in a wide range of research traditions, including learning, foraging, and decision making literatures. Here, we integrate findings from these and other often-isolated literatures in order to gain a better understand- ing of the possible tradeoffs between exploration and exploitation, and we propose new theoretical insights that might guide future research. Specifically, we explore how potential tradeoffs depend on (a) the conceptualization of exploration and exploitation; (b) the influencing environmental, social, and individual factors; (c) the scale at which exploration and exploitation are considered; (d) the relationship and types of transitions between the 2 behaviors; and (e) the goals of the decision maker. We conclude that exploration and exploitation are best conceptualized as points on a continuum, and that the extent to which an agent’s behavior can be interpreted as exploratory or exploitative depends upon the level of abstraction at which it is considered.

Mediation analysis with structural equation models: Combining theory, design, and statistics

Abstract: Statistical tests of indirect effects can hardly distinguish between genuine and spurious mediation effects. The present research demonstrates, however, that mediation analysis can be improved by combining a significance test of the indirect effect with assessing the fit of causal models. Testing only the indirect effect can be misleading, because significant results may also be obtained when the underlying causal model is different from the mediation model. We use simulated data to demonstrate that additionally assessing the fit of causal models with structural equation models can be used to exclude subsets of models that are incompatible with the observed data. The results suggest that combining structural equation modeling with appropriate research design and theoretically stringent mediation analysis can improve scientific insights. Finally, we discuss limitations of the structural equation modeling approach, and we emphasize the importance of non-statistical methods for scientific discovery.