Research Proposal: Individual Differences and Instance Based Decision Making

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Table of Contents
CHAPTER I: INTRODUCTION AND BACKGROUND INFORMATION 4
1.1 Introduction 4
1.2 Rationale/justification 5
1.3 Research aim and objectives 6
CHAPTER II: LITERATURE REVIEW 8
2.1 Introduction 8
2.2 Decision making 8
2.3 Risk perception and the dual Process theories 9
2.4 Emotions in decision making 12
CHAPTER III: METHODOLOGY 14
3.1 Introduction 14
3.2 Research Design 14
3.3 Materials 14
3.4 Measures of decision making behavior 15
3.5 Work Plan/ Time schedule inform of a Gantt chart 17
References 19

CHAPTER I: INTRODUCTION AND BACKGROUND INFORMATION
1.1 Introduction
In the recent past, more attention has been provided in the area of decision making in an effort to understand and explain human rationality. According to Lerner, Li, Valdesolo, and Kassam, (2015), the study of human decision-making has emerged as unique area of study as well as the meeting point where other traditional fields such as psychology and economics combine and exchange knowledge on how human make decisions when facing multiple choices. Both cognitive psychology and economics have been substantially long collaborators in the study of how human agents make decisions (O’Donovan et al., 2015).
Economic theory, for example, suggests that human behavior behind market efficiency is mainly influenced by factors such as material incentives, which implies that financial decision making behavior is therefore is primarily driven by rationality. As a result, extrinsic incentives which are usually based on personal profit are believed to significantly shape the decision making behavior. According to Betsch and Haberstroh (2014), economic theory assumes that human decision-making agents are basically rational actors which are motivated exclusively by self-interest, and their basic goal is to exploit their utility irrespective of the context when upon to make a decision.
In cognitive neuro-scientific and cognitive psychological researches conducted in the recent past, human decision making agents are perceived to be cognitive information processing systems that code and process available information in a rational/cautious way as well as in an irrational/ unconscious way. Nevertheless, empirical evidence shows that human decision making is mainly influenced by less cautious factors that have not been studied, recorded, as well as documented. The unconscious factors believed to be guiding human decision making throughout the history are known as cognitive or simple heuristics (O’Donovan et al., 2015).
1.2 Rationale/justification
According to O’Donovan et al. (2015), the behavioral decision theory is one of the main contributors to this multidisciplinary effort. This theory provides a systematic and consistent approach that explains the numerous cognitive and systematic reproducible errors made by human decision making agents in an attempt to solve simple and complex decision problems and choice-dilemmas. This theory brings in psychological insights to the accepted utilitarian frameworks provided by economists, statisticians, and mathematicians in the quest to understand, systematize, and model human rationality. Nevertheless, analysis of extant literature shows that some crucial concepts still escape, particularly the concept of decision making under uncertainty and risk (Betsch & Haberstroh, 2014).
In the past five decades, behavioral economists, that is; social scientists who focus on providing psychological insights into the study of economic decision making have consistently challenged the conventional economic view of human beings as rational decision making agents and have provided am empirical understanding of economic behavior and decision making. Beach (2014) suggests that the study of individual differences such as impulsivity, affective responses to awaiting reward and punishment, as well as emotional and affective states among other differences involved in decision making can help provide crucial explanatory power in the understanding of human rationality (Beach, 2014).
Incorporating individual differences in the study of decision-making processes and human rationality may also help in the evaluation of the current decision theoretical models, inclusive of both descriptive and normative frameworks, which as Beach (2014) suggests bear important limitations in respect to minimizing effects irrational economic behavior while at the same time guaranteeing consistency and tractability. Nonetheless, the field of individual differences has been neglected in most of the attempts that have been made by behavioral economists, psychologists, and economic theorists in an effort to enlighten and model economic behavior.
As Beach (2014) noted, though psychologists and behavioral economists have played a significant role in shaping the present day decision making field, there has been substantially little contribution from the field of individual differences. This might have resulted from the fact that behavioral economists have in the past emphasized primarily on the basic principles that drive aggregate economic behavior. As a result, there has been a significantly low number of studies that have focused on the individual differences, risk taking behavior, as well as decision making both in experimental and social contexts, an aspect that has led to low contribution into the study of risk taking behavior from the psychology perspective.
According to Zsambok and Klein (2014), individual differences are a crucial aspect in the multidisciplinary effort to help in explaining the current inconsistency in findings made in aggregate economic behavior. The claim offers support that the proposal that economists should consider use a multi-domain approach in measuring people’s attitudes to financial decision making and risk taking. This rationale confirms the need for carrying out a fresh study in the field of people’s attitudes towards risk and financial decision making while taking into consideration individual differences in experimental and real-world risk taking behavior.
1.3 Research aim and objectives
Based on the statement of the problem and rationale highlighted in the previous section, the main aim of the proposed study will be to investigate the correlation between individual differences (personality traits, emotional intelligence, and affective states) with decision making processes in uncertain and risky domains. In this regard, the researcher will seek to meet the following research objectives.
 To identify factors influencing financial decision-making and risk taking behaviour; and
 To find out whether individual differences such as emotional intelligence (EI), personality traits, and affect predict instance-based risk taking behaviour.
In the process of meeting the above-highlighted research objectives, experimental evidence relating to the influence of personality traits and affect on preference variation of risk taking behavior in instance-based monetary decision-making tasks will be provided. Experimental evidence on the correlation between financial decision making under uncertainty and risk will also be provided. For the mentioned research objectives to be fully met and achieve meaningful results, the research team will be required to have adequate knowledge in at least three independent research fields namely, cognitive psychology, behavioral economics, and individual differences.
CHAPTER II: LITERATURE REVIEW
2.1 Introduction
The purpose of this chapter is to present a review of the currently available literature about behavioral and cognitive decision making. In this chapter, the main concepts covered in the study of decision making as well as crucial links for understanding the essence of merging individual differences, cognitive psychology, and economic theory for a better understanding of human decision making process are covered. The presented literature together with key findings made in this area of research from previous studies are presented inform of themes, rather than a discussion of individual research.
2.2 Decision making
The topic of human decision making process has for many years drawn interest of practitioners and researchers from a wide range of disciplines, particularly from psychology, philosophy, neuroscience, and economics to mention just a few. The main findings made in all these disciplines can be summarized in an effort to systematically scrutinize cognitive agents’ preferences, judgments, and choices so as to have a better understanding of the role these agents play in reasoning and human decision making process (O’Donovan et al., 2015). Merging findings from all these domains can help in assessing how good human beings are in making decisions, a dilemma that has been reported in most of the recently carried out studies in all disciplines of decision making research.
According to Zsambok and Klein (2014), behavioral decision research highlights numerous key findings that indicate a collection of reproducible cognitive errors and universal limitations in human decision making. These errors and limitations unconsciously influence or impact the decision making process and as O’Donovan et al. (2015) argues, it is difficult to avoid them in spite of being warned about these errors and limitations before making a judgment.
Over the years, the main reproducible cognitive errors, that is; signs of irrationality in human judgment, process of decision making, and problem solving have been categorized into a number of theoretical frameworks that overlap. The frameworks are broadly grouped into two namely, cognitive psychological models and evolutionary psychology models where the former is primarily based on the Kahneman and Tversky (1991) grouping of reproducible cognitive errors, while the latter follows arguments by evolutionary psychologists such as Gigerenzer and Goldstein (1996); Gigerenzer et al. (1999); and Gigerenzer (2000).
Judgment errors in human reasoning based on evolutionary psychology models follow a slightly different paradigm that underlines the adaptive origin of the errors in the human process of making decision. However, due to the scope of this proposal, this chapter provides a brief overview of the influence of emotions on the decision making process as well as the dual process theories where the two systems of human cognitive architecture are used to represent risk analysis and risk feeling.
2.3 Risk perception and the dual Process theories
Most of the decisions involve the probability of losing or gaining, commonly known as risk, and such probabilities encompass emotional provocation in the face of anticipated outcome. According to Fiske and Taylor (2013), different people interpret the term ‘risk’ differently and their notions towards risk cannot be measured numerically since they are inherently associated with a natural human reaction or fear. Though there is no ideal theory that explains how different persons perceive risks and evaluate their decisions and judgments involving risks, research shows that human understanding of the complexity of risk has consistently increased over the years (Fiske & Taylor, 2013).
According to Schoenfeld (2015), how an individual perceives risk is an essential part of an intricate cognitive mechanism that influences decision making process in an effective manner, rather than in a rational way, because human decision making agents inevitably translate risk into dangers, hence associate with the fear. Currently, there are two main cognitive systems namely, ‘System 1’ and ‘System 2,’ systems that help in differentiating between intuitive processing and analytic reasoning. According to Schoenfeld (2015), heuristics are part of the first system and rationality is part of the second system.
‘System 1’ is reported to be effortless, intuitive, habitual, associative, rapid, automatic, parallel processing and it is reported to the one that helps human beings to survive by adopting survival mechanisms that respond to risky situations. The second system is deliberately controlled, effortful, slow and serial, requires self-awareness, is deductive, and it encompass use of normative rules and complex algorithmic calculations.
The first system depends on cognitive shortcuts and processing of images, and it is associated with experience of emotions where it associates potential risks with feelings and emotional states. The system informs human whether the environment is safe or not. The system helps human beings in evaluating formal risk, calculating possibilities and in offering formal logical explanations. The second system, on the other hand, represents risk as an analysis (Lee & Schwarz, 2014).
Steinhart, Kamins, and Noy (2013) pinpoints that the two systems of reasoning operate dependently since interplay between emotions and reasoning are essential in rational behavior. Steinhart, Kamins, and Noy (2013) further indicate that the two operate in parallel way where each decision processing model relies on the other for drive and feedback. A given mental process is assigned to either system 1 or system 2 based on the indications provided by the difference in cognitive processing. Nevertheless, Lee and Schwarz (2014) argue that effortful processes disrupt each other, while effortless processes do not suffer or cause much interference when merged with other tasks because the computational power of human brains with respect to information processing is limited.
A collection of studies from various disciplines such as cognitive psychology, neuropsychology, and neurology have indicated that rational decision making cannot be efficient and fruitful except when it is guided by emotional and intuitive processing. Though extensive research has been conducted on theoretical modeling relating to the dual process theories, it is evident that dual system frameworks of decision making under uncertainty and risk based on the dual system aspect discussed in this section are yet to be established (Lee & Schwarz, 2014).
The very first researcher to investigate and discuss the essence of affective processing was Zajonc (1998) who argued that application of emotional reactions to stimuli are the first reactions that occurs automatically to guide decision making, and judgment. The researcher also indicated that individuals’ perceptions encompass affective reactions. Researchers who conducted their studies later made related arguments with, for example, Cushman (2013) concluded that indeed risk taking behavior can be affected by the decision agents’ affective states in various settings and environments.
In line with these findings, Gorlin and Dhar (2013) noted that people’s risk perception under the guidance of affective reactions can drive judgments and lead to decisions without including relevant information processing, that is; a systematic and balanced assessed of the available alternative choices. The researcher also argued that people generally depend on affect when making decisions and judgments about risks and benefits, even though this argument has been criticized in recent studies with some researchers terming the claim as debatable (Lee & Schwarz, 2014).
2.4 Emotions in decision making
The dominant role of emotions in the decision making process is another crucial domain in the behavior decision theory. According to Lerner, Li, Valdesolo, and Kassam (2015), decision making researchers and lay people have over years overlooked or probably did not know that human decision making process and judgment is significantly influenced and guided by emotions. In the last two decades, an increasing body of scientific evidence has shown that emotions play an instrumental role in human decision making process.
For many years, emotions have been described as a bad consultant to human decision making process and reasoning. However, researches carried out in the recent past have repeatedly shown that the brain part responsible for emotions informs and impacts reasoning. In addition, modern research has revealed that ability to reason and make decisions is impaired when the part of the brain in charge of mediating emotional information is isolated from the frontal part of the brain responsible for executive functioning, commonly known as ventromedial prefrontal cortex (Lerner, Li, Valdesolo, & Kassam, 2015).
Research carried out in persons with severe damage in the ventromedial prefrontal cortex has shown a major reduction of risk avoidant behavior, which is usually present in normal human participants. It has also emerged that such patients experience profound abnormalities when processing emotions and feelings and that the level of abnormality hinders emotional engagement as far as complex social events and situations are concerned. These findings have been confirmed in numerous experiments and they are currently considered as the cornerstone of the somatic marker (SM) hypothesis (Lerner, Li, Valdesolo, & Kassam, 2015).
It is also important to note that even problem solving, decision making, and selection of choices that appear rational and intellectual are processed under direct influence of emotional signals that according to Cushman (2013); and Gorlin and Dhar (2013) interact in a complex manner with the decision making process. Therefore, it is fair to conclude that emotional regulating mechanisms network in a multifaceted way and affect human reasoning, judgment, and decision making process.
However, certain issues relating to this topic remain ambiguous and there are numerous questions that need to be answered (Lerner, Li, Valdesolo, & Kassam, 2015). In addition, more research is required in this field so as to ascertain and integrate all the findings in a single experimental paradigm that would indicate the general and descriptive model of human decision making behavior and judgment.

CHAPTER III: METHODOLOGY
3.1 Introduction
This chapter presents a detailed methodology framework that will be employed by the researcher in the course of this study. The methodology framework entails a detailed discussion of the various methods that the researcher will use to conduct this study. The main aim of this study is to examine the factors that affect personal decision-making process and establish if the individual difference in emotional intelligence affects a person decision-making process.
3.2 Research Design
According to Creswell (2013) a research design is a systematic plan that outlines and describes the procedure used to carry out a specific study. For the proposed study, the researcher will use quasi-experiment research design. A quasi-experiment research design has been chosen for the proposed study because it allows the researcher to control assignment to the treatment condition.
For the proposed study, the investigator will use a sample size of 93 participants who will be recruited randomly from the University. Participants will be assessed using binary choice tasks and double gambling risk taking tasks where they will be requested to fill in trait emotional intelligence questionnaire and a self-report measure that is based on deskman’s impulsivity intervention. The self-report measure is also based on Gray’s behavior activation and behavior activation systems theory that help in differentiating between functional and dysfunctional impulsivity (Fuentes et al., 2012).
3.3 Materials
3.3.1 Emotional intelligence measure: TEIQueSF
For the purpose of this study, the researcher will use the trait emotional intelligence questionnaire for exploratory studies. TEIQueSF refers to a 30-item scale that was developed to measure and assess the global trait emotional intelligence. The short form measure is derived by extracting some items from the full form also known as TEIQue. TEIQue is a 150 item questionnaire that is used to measure emotional intelligence.
3.3.2 Emotional regulation measure
For this study, the researcher will also conduct a 10-question questionnaire designed specifically to measure emotional regulation. The ten-item questionnaire used for this study will be developed using ten questions selected from TEIQue. The EMRE is the scale that will be used measure emotional regulation (Singh, 2015). The scale shows the extent to which a person can control his emotion. When using the scale, high score suggests that that particular individual is highly aware of his/her emotional state while a low score suggest that a person is less aware of his/her emotional state.
3.3.3 Personality measure: behavioral activation and behavioral inhibition
Scales
The behavioral activation and inhibition scale refers to a 24-item self-report measure that was developed for measuring and assessing Gray’s personality model of behavioral inhibition and behavioral activation. The BIS scale is a measure of behavioral inhibition and it consist of seven items. On the other hand, the behavioral activation scale is further subdivided into 3 subscales: the reward responsive that is made up of 5 items, drive contains 4 items and fun seeking which is also made up of 4 items (Singh, 2015).
3.4 Measures of decision making behavior
Study 1-A Task: The binary choice task
This will be a version of a short choice-task consisting of 16 binary choice gambles. The gamble is of the form choosing between A) $120 or B) a 60% probability of winning $300 for a total of 64 gambles. For this study, chances of winning will be denoted by letter ‘P’ while the section of choice will be denoted by letter ‘Y’. The amount will be calculated using the formula X=Y.P^1/ ƛ. From the above stimuli, the four values presented will represent the different levels of risk aversion and they will be used to determine the participants risk behavior in this decision-making task.
Study 1-B Task: The binary double gamble task
In this study, the double gamble task will be borrowed from Rogers and colleagues (1999) but it will slightly be modified so as to align it to the task in Study 1-A. As a result, the study will be able to yield different levels of risks and different probability for the two options. From the guidelines set in Task 1-A, there will be four version of the short choice task that will consist of 16 binary choices.
3.5 Work Plan/ Time schedule inform of a Gantt chart
The proposed research study will be conducted in a period of three months as shown in the time schedule below and the researcher will strictly adhere to the time table. (You can edit the timetable to make it suit the provided time frame)
Task to be performed Nov 1- 15th 2015 Nov 15th – Dec 15th Dec 15th – Dec 30th Jan 1st – Jan 15th Jan 15th – Jan 24th, 2015
1. Finalised research
Proposal
2. Data collection
3. Prepare for data entry
4. Data cleaning and
Preliminary analysis
5. Data analysis and
Report writing
6. Discuss
Recommendations/ plan of action
7. Finalize report
8. Presentation and dissemination

References
Beach, L. R. (2014). Decision making in the workplace: A unified perspective. Psychology Press.
Betsch, T., & Haberstroh, S. (Eds.). (2014). The routines of decision making. Psychology Press.
Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Cushman, F. (2013). Action, outcome, and value a dual-system framework for morality. Personality and social psychology review, 17(3), 273-292.
Fiske, S. T., & Taylor, S. E. (2013). Social cognition: From brains to culture. Sage.
Fuentes, P., Barrós-Loscertales, A., Bustamante, J. C., Rosell, P., Costumero, V., & Ávila, C. (2012). Individual differences in the Behavioral Inhibition System are associated with orbitofrontal cortex and precuneus gray matter volume. Cognitive, Affective, & Behavioral Neuroscience, 12(3), 491-498.
Gigerenzer, G. (1996). On narrow norms and vague heuristics: A reply to Kahneman and Tversky.
Gigerenzer, G. (2000). Adaptive thinking: Rationality in the real world. Oxford University Press.
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62, 451-482.
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650.
Gigerenzer, G., & Todd, P. M. the abc Research Group 1999. Simple heuristics that make us smart.
Gorlin, M., & Dhar, R. (2013). Refining the dual-process theory of preference construction: A reply to Gawronski, Martin and Sloman, Stanovich, and Wegener and Chien. Journal of Consumer Psychology, 23(4), 564-568.
Lee, S. W., & Schwarz, N. (2014). Metaphor in judgment and decision making. Metaphorical thought in social life. Washington, DC: American Psychological Association.
Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and Decision Making: Online Supplement. Annu. Rev. Psychol, 66, 33-1.
O’Donovan, J., Tintarev, N., Felfernig, A., Brusilovsky, P., Semeraro, G., & Lops, P. (2015, September). Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (# IntRS). In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 347-348). ACM.
Rogers, R. D., Owen, A. M., Middleton, H. C., Pickard, J., & Robbins, T. W. (1999). Decision-making in humans activates multiple sites within orbital prefrontal cortex: a PET study. J Neurosci, 20, 9029-9038.
Schoenfeld, A. H. (2015). How we think: A theory of human decision-making, with a focus on teaching. In The Proceedings of the 12th International Congress on Mathematical Education (pp. 229-243). Springer International Publishing.
Singh, T. (2015). Examining the mediating effect of academic performance on the relationship between emotional intelligence and campus placement success among management students. Journal of Strategic Human Resource Management, 3(2).
Steinhart, Y., Kamins, M. A., Mazursky, D., & Noy, A. (2013). Thinking or feeling the risk in online auctions: the effects of priming auction outcomes and the dual system on risk perception and amount bid. Journal of Interactive Marketing, 27(1), 47-61.
Zajonc, R. B. (1998). Emotions: in Handbook of Social Psychology. New York: Oxford University Press
Zsambok, C. E., & Klein, G. (2014). Naturalistic decision making. Psychology Press.
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