Social phobia is maintained in part by judgmental biases concerning the probability and cost of negative social events. One hypothesized mechanism of action of cognitive behavioral therapy (CBT) for anxiety disorders is its reduction in the exaggerated probabilities and costs associated with feared outcomes, termed the “cognitive mediation hypothesis” (Foa & Kozak, 1986). A number of studies have examined the cognitive mediation hypothesis; some investigations find cost bias to be more important to treatment outcome, whereas others find probability bias to be more important. However, methodological limitations of several of these studies leave open the possibility that changes in judgmental biases are simply correlates or consequences of social anxiety reduction. Attentional processes, which mark the first discrimination of incoming information, may serve as precursors to cognitive processes like probability and cost estimates. Though intuitively linked, whether social phobics’ pattern of attending to external threat cues is correlated with their appraisals of the cost and probability of negative events has yet to be examined empirically. The current project examines cost and probability biases and their relation to attention bias and treatment outcome in a randomized controlled trial of CBT for social phobia. It was found that, contrary to hypotheses, greater attentional bias for threat in either direction (vigilance or avoidance) did not predict higher cost and probability estimates. However, a significant relation was observed between attentional vigilance and outcome probability estimates, such that greater vigilance for threat predicted greater estimates of the likelihood that negative social events will occur. As hypothesized, early changes in cost and probability biases predicted later changes in social anxiety symptoms (and not vice versa). Changes in probability estimates were a stronger predictor of treatment outcome than changes in cost estimates. Broadly, findings provide support for the cognitive mediation hypothesis of social phobia and point to both outcome cost and outcome probability as potential treatment mechanisms. Findings are discussed in the context of extant theories of social phobia, and directions for future research are proposed.

In an era increasingly shaped by artificial intelligence (AI), the necessity for unbiased decision-making from AI systems intensifies. Recognizing the inherent biases in humandecision-making is evident through various psychological theories. Prospect Theory, prominently featured among them, utilizes a probability weighing function (PWF) to gain insights into human decision processes. This observation prompts an intriguing question: Can this framework be extended to comprehend AI decision-making? This study employs a systematic Gibbs sampler method to measure probability weighing function of AI and validate this methodology against a dataset comprising 1 million distinct AI decision strategies. Subsequently, exemplification of its application on Recurrent Neural Networks (RNN) and Artificial Neural Networks (ANN) is seen. This allows us to discern the nuanced shapes of the Probability Weighting Functions (PWFs) inherent in ANN and RNN, thereby facilitating informed speculation on the potential presence of “probability bias” within AI. In conclusion, this research serves as a foundational step in the exploration of "probability bias" in AI decision-making. The demonstrated reliability of the systematic Gibbs sampler method significantly contributes to ongoing research, primarily by enabling the extraction of Probability Weighting Functions (PWFs). The emphasis here lies in laying the groundwork –obtaining the PWFs from AI decision processes. The subsequent phases, involving in-depth understanding and deductive conclusions about the implications of these PWFs, fall outside the current scope of this study. With the ability to discern the shapes of PWFs for AI, this research paves the way for future investigations and various tests to unravel the deeper meaning of probability bias in AI decision-making.

Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions’ makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research tool. To see the impact of training, one group of the participants received the scenarios without training and the other group received them with training. The training consists of a brief description of the cognitive bias as well as an example of the cognitive bias. Cognitive reflection is operationalized using cognitive reflection test (CRT). The survey was distributed to students at University of North Texas (UNT). Logistic regression has been employed to analyze data. The research shows that participants show the cognitive biases proposed by Tversky and Kahneman. Moreover, CRT is significant factor to predict the cognitive bias in two scenarios. Finally, providing training in terms of warning helps participants to make more rational decisions in 4 scenarios. This means that although cognitive biases are inherent in the mind of people, management of corporations has the tool to educate its managers and professionals about such biases which helps companies make more rational decisions.