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One of the biggest challenges for the modern society is that of climate change. Despite the growing accumulation of scientific evidence that points towards a strong need for action to be made regarding anthropogenic (human made) climate change, there is a lack of unity in what actions are needed and an outspread passivity amongst both establishments and the public. The reason behind this is attributed to lack of belief in anthropogenic climate change, and lack of pro-environmental attitudes amongst the public. Furthermore, these issues have been found to relate to identity related beliefs and attitudes that conflict with pro-environmental beliefs and attitudes, where political orientation has proven to be a strong factor. One way of dealing with these issues could be through informational influences. By presenting people to information shaped in different ways, one could increase the belief in anthropogenic climate change and pro-environmental attitudes. This study examined the relationship between three different informational influences, and its potential effect on climate change beliefs and attitudes. Furthermore, this study examined the relationship between demographic factors such as age, gender and political orientation with regards to their potential effect on climate change beliefs and attitudes. 449 participants completed a survey with intent to measure the potential effects informational influences and demographic factors had on climate change beliefs and attitudes. Despite that indications where found, no significant results could be identified for the informational influences. All demographic factors had some significant effect on climate change beliefs or attitudes, where political orientation was the strongest influencing factor. This relates to earlier research and further implications were discussed for future studies.

abstract: Prior research suggests that people ignore evidence that is inconsistent with what they want to believe. However, this research on motivated reasoning has focused on how people reason about familiar topics and in situations where the evidence presented interacts with strongly-held prior beliefs (e.g., the effectiveness of the death penalty as a crime deterrent). This makes it difficult to objectively assess how biased people are in motivated-reasoning contexts. Indeed, recent work by Jern and colleagues (2014) suggests that apparent instances of motivated reasoning may actually be instances of rational belief-updating. Inspired by this new account, the current studies reexamined motivated reasoning using a controlled categorization task and tested whether people assimilate evidence differently when they are motivated to maintain a certain belief versus when they are not. Contrary to earlier research on motivated reasoning, six studies with children and adults (N = 1295) suggest that participants’ motivations did not affect their information search and their beliefs were driven primarily by the evidence, even when the evidence was incongruent with their motivations. This work provides initial evidence for the account proposed by Jern and colleagues. / Dissertation/Thesis / Masters Thesis Psychology 2019

[Abstract]: This dissertation addresses several highly-critical issues in affective computing and agent architecture design including knowledge representation, motivation, emotion appraisal and affective decision making. The approach presented integrates motivational drives, goals and associated behaviours via a multi-dimensional Affective Space. The research focuses on an emotionally motivated artificial intelligence (EMAI) architecture. This architecture dispenses with the ideas implemented in contemporary affective agent architectures where individual emotional states are modelled as individual variables, integrated and processed using complex algorithms. Contemporary approaches required significant programming effort to modify them for domains outside their realm, integration of new emotional states and high-level complex affective decision making. Unlike contemporary affective agent architectures, the EMAI architecture reasons using a multi-dimensional decision making process where emotional states are modelled as coexisting locations in a six-dimensional affective continuum called the Affective Space. Through use of the Affective Space, an EMAI agent can predict the effect that certain behaviours will have on its emotional state and in turn decide how to behave. Furthermore, the agent can use the emotions produced from its behaviour to update its beliefs about particular events and entities. The nature of the Affective Space also allows an EMAI agent to deal with processes related to emotion synthesis in a more effective manner than contemporary architectures. These processes include the natural diminishing of an emotional state's strength over time, the way in which emotions can influence an agent's perspective of a situation and the way in which an agent can migrate from one emotional state to another. This dissertation contributes crucial and unique concepts and formalisations of emotion based intelligence for agent construction to the domain of Artificial Intelligence (in particular Affective Computing). It introduces a unique process for emotionally motivated decision making based on holistic and atomic appraisals made with respect to events. The thesis contained within has been supported through experimentation that has confirmed the effectiveness of the emotion synthesis technique in the EMAI architecture and how this is used to produce intelligent agents capable of emotional reasoning and decision making.

In this study, I examine the effects of audit guidance and estimation uncertainty on auditors’ confirmation bias and professional skepticism when evaluating fair value estimates. Fair value estimation is becoming more prevalent in financial reporting frameworks, and regulators warn that fair value estimation presents higher risk of material misstatement when greater judgment in estimation is involved. In addition recent evidence from the Public Company Accounting Oversight Board (PCAOB) indicates that some auditors may not be exercising sufficient professional skepticism when performing audit procedures in higher risk areas of the audit. Martin et al. (2006) suggest that it may be the audit standards themselves that orient auditors toward biased evaluation of management’s estimates, suggesting that such directional audit guidance leads to confirmation bias. Further, it is possible that because of auditors’ intolerance for ambiguity, that a greater degree of estimation uncertainty exacerbates the bias. Thus, I examine whether directional audit guidance (e.g., support management’s estimate, and oppose management’s estimate) versus non-directional audit guidance (e.g., develop own estimate) affects auditors’ confirmation bias differentially under varying degrees of uncertainty (e.g., low vs. high), and the extent to which this bias increases or decreases professional skepticism. The results show that auditors exhibit the greatest confirmation bias when they are directed to oppose versus support management’s estimate or generate their own estimate, and that this bias increases the degree of professional skepticism exercised by auditors. Further, the greatest extent of confirmation bias resulted when auditors were directed to oppose management’s estimate and estimation uncertainty was high. This study sheds light on the effects of directional versus non-directional audit guidance in the presence of uncertainty and should be informative to standard setters and practitioners as they press forward in issuing new audit guidance related to the evaluation of fair value estimates.

This study examines the effect of motivation on political selective exposure and selective perception using an online experiment. Studies have found that though people have a preference for like-minded political information over counter-attitudinal information, they do not avoid counter-attitudinal political information altogether (Garrett, 2009; Garrett, Carnahan, & Lynch, 2011; Stroud, 2008). This study examines under what conditions people are likely to expose themselves to more like-minded information than counter-attitudinal information and under what conditions people are likely to seek out more counter-attitudinal information than like-minded information. Based on the theory of motivated reasoning and Hart et al. (2009)'s model, I proposed a model that explained selective exposure and selective perception based on motivation. Defense motivation, the motivation to hold attitude-consistent cognitions with one's original attitudes, beliefs, or behaviors, was predicted to increase selective exposure and selective perception. Accuracy motivation, the motivation to arrive at the correct conclusion, was predicted to reduce selective exposure and selective perception. Finally, information utility motivation, the motivation to choose information that has the highest utility, was predicted to reduce selective exposure when counter-attitudinal information was equally useful as attitude-consistent information, but increase selective exposure when attitude-consistent information was more useful than counter-attitudinal information. In both cases, it was predicted that the selective perception pattern would not be changed. The study also tested the additive effect of the three motivations and examined which motivation can override other motivations in determining selective exposure and selective perception. Results showed that accuracy motivation was effective in reducing selective exposure for both strong partisans and those who were not strong partisans. Accuracy motivation can override defense motivation in affecting selective exposure. Information utility alone, defense motivation alone, and the combination of the three motivations produced mixed results. Accuracy motivation was effective in reducing selective perception for those who were not strong partisans. The link between selective exposure and selective perception was not found.

In the rapidly changing world of today, people have to make decisions under some degree of uncertainty. At the same time, the development of computing technologies enables people to take uncertain factors into considerations while making their decisions.Stochastic programming techniques have been widely applied in finance engineering, supply chain management, logistics, transportation, etc. Such applications often involve a large, possibly infinite, set of scenarios. Hence the resulting programstend to be large in scale.The need to solve large scale programs calls for a combination of mathematical programming techniques and sample-based approximation. When using sample-based approximations, it is important to determine the extent to which the resulting solutions are dependent on thespecific sample used. This dissertation research focuses on computational evaluation of the solutions from sample-based two-stage/multistage stochastic linear programming algorithms, with a focus on the effectiveness of optimality tests and the quality ofa proposed solution.In the first part of this dissertation, two alternative approaches of optimality tests of sample-based solutions, adaptive and non-adaptive sampling methods, are examined and computationally compared. The results of the computational experiment are in favor of the adaptive methods. In the second part of this dissertation, statistically motivated bound-based solution validation techniques in multistage linear stochastic programs are studied both theoretically and computationally. Different approaches of representations of the nonanticipativity constraints are studied. Bounds are established through manipulations of the nonanticipativity constraints.

Group commitments such as partisanship and religion can bias the way individuals seek information and weigh evidence. This psychological process can lead to distorted views of reality and polarization between opposing social groups. Substantial research confirms the existence and persistence of numerous identity-driven divides in society, but means of attenuating them remain elusive. However, because identity-protective cognition is driven by a need to maintain global and not domain specific integrity, researchers have found that affirming an unrelated core aspect of the self can eliminate the need for ego defense and result in more evenhanded evaluation. This study proposes a competing intervention. Individuals possess numerous social identities that contextually vary in relative prominence; therefore a different means to unbiased cognition may be to make many social identities salient simultaneously, reducing influence of any potentially threatened identity. This may also reduce selective exposure to congenial information, which has not been found with affirmation. This study also advances research on the phenomenon of selective exposure by considering individuals’ interpersonal networks in information search. Because networks are not static, and are instead contextually activated, inducing a more complex representational structure of the self may broaden the set of contacts from whom individuals seek information. The bias-mitigative potential of self-affirmation and social identity complexity is examined here in a series of dispute contexts — two partisan, one religious — over a mining spill, an advanced biofuels mandate, and gene editing technology. Results from the three experiments (total N = 1,257) show modest support for social identity complexity reducing group-alignment of beliefs, behavior, and information search, while affirmation failed to reduce, and in some cases increased, group alignment.