Behavioral economics for decision support systems researchers

This commentary examines the historical importance of decision support to the information systems (IS) field from the viewpoint of four researchers whose work spans the several decades of decision support systems (DSS) research. Given this unique “generational” vantage point, we present the changes in and impact of DSS research as well as future considerations for decision support in the IS field. We argue that the DSS area has remained vital as technology has evolved and our understanding of decision-making processes has deepened. DSS work over the last several years has contributed both breadth and depth to decision-making research; the challenge now is to make sense of it all by placing it in an understandable context and by applying our analysis to the relevant issues looming in the future. One major outcome of this commentary is the identification of future trends in DSS research and what the users of these new DSS outlets can learn from the past. Trends include the increasing impact of social and mobile computing on DSS research, as well as knowledge management DSS and negotiation support systems that shift the focus to delivering more customer-centric and marketplace support.

The objective of this chapter is to demonstrate the use of a decision support systems research (DSSR) framework to improve decision making support systems (DMSS) quality. The DSSR Framework, which was developed to integrate theoretical constructs from various information systems areas into a coherent theme, can serve as a tool for DMSS developers. Developed to provide a unified reference to theoretical constructs used in theory building and testing, the DSSR framework can also be used as the basis for the identification and selection of a hierarchy of factors potentially affecting the quality of DMSS development. The chapter proposes that a unified set of quality factors derived from the DSSR framework be used in tandem with the generic software quality metrics framework specified in IEEE Standard 1061-1992. The integration of these two frameworks has the potential to improve the process of developing high-quality decision making support systems and system components. The usage of these frameworks to identify system quality factors is demonstrated in the context of a military research and development project.

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

Decision-making can be the difference between life and death in all types of aviation, but in general aviation (GA), where most of the flying is conducted as single-pilot operations, the decision-making of one individual becomes fundamentally important. It is critical to consider, first, why pilots make bad decisions that can ultimately lead to weather-related aviation accidents or incidents; and second, whether a better understanding of weather-related decision-making can inform regulations that will improve decision-making and consequently reduce the frequency of pilot-error accidents. Behavioral economics (BE) aims to better understand individual decision-making to model decision-making pathways. As individual decision-making is central to aviation safety, better modeling of decision-making pathways should be a central aim not just for pilots, but also for aviation regulators, such as the Civil Aviation Safety Authority (CASA) in Australia. While there has been little analysis of pilot decision-making using BE, we argue that BE, with its focus on predictive models of individual decision-making, provides a rich framework to understand pilot decision-making and inform more targeted regulation. This argument is in four parts. The first part identifies that there is an ongoing safety issue with visual flight rules (VFR) pilots flying into instrument meteorological conditions (IMC). The second part introduces some of the core concepts of BE, such as the rejection of perfect rationality and the reliance upon certain behavioral biases in decision-making. We argue that VFR into IMC is an appropriate context in which to apply BE as there is an identifiable measure of a pilot’s welfare and concerns around paternalism fall short when dealing with protecting the welfare of those likely to be impacted by a pilot’s decision-making, such as passengers and aircraft owners. The third part reviews the existing research applying behavioral models of decision-making in respect of VFR into IMC and identifies three behavioral biases that—among others—can lead to poor decision-making: (i) environmental literacy, (ii) overconfidence, and (iii) prospect theory. The final part briefly introduces some potential avenues for BE to inform regulatory reform, including better education of pilots and regulators in respect of the psychological factors to which pilots may fall victim, as well as more directed training for pilots to address the environmental literacy concerns identified in this part. We conclude that the regulatory environment should be reformulated to adequately account for predictable behavioral biases.

The purpose of this essay is to help generate a framework to promote and refine further investigations and projects in the field of behavioral economics (BE). It leads to being useful for delimiting the phases of any investigation and defines the conditions and elements to be considered in a genuine behavioral study. It has been elaborated by reviewing the work of behavioral insight units (nudge units) of countries and international organizations during the last decade, as well as previous papers in the discipline, to offer a summary of the main steps in BE studies: a manageable shortlist of cognitive bias, feasible examples of application, a guide to know when to nudge, and a reminder of what nudge is not; in short, a process to nudge extracted from the practice of the specialists who applied the approach in the last decade. Particularly, it can result in useful steps for those researchers willing to apply the behavioral approach to their ongoing studies and projects, besides students who require familiarizing with the method.

Behavioral Law & Economics Goes to Court: The Fundamental Flaws in the Behavioral Law & Economics Arguments Against No-Surcharge Laws

Marine Natsvaladze
 Ivane Javakhishvili Tbilisi State University
 Georgia
 https://orcid.org/0000-0002-7635-8565
 
 The Covid-19 pandemic has led to unprecedented changes in people's social and economic behavior, They had to struggle to maintain their health and earn a living at the same time. Behavioral economics has been found to be highly relevant in explaining the behavior of economic agents during the Covid-19 pandemic, under the "new normal" conditions to explain real human economic behavior [9].
 Behavioral economics is an important paradigm in modern economics, and it has revolutionized the standards of economic theory and instead of rational decisions, it offers more realistic alternatives for decision-making and management - any decision should be based not on rational and mathematical calculations, but on the study of human behavior patterns, the analysis of causes and the awareness of whether how people think and how we can influence their choices by changing the content or environment during the decision-making process, without restricting the choices (Using "Nudge Theory" of behavioral economics).
 The Covid-19 pandemic has raised several questions about how market participants are responding to the global pandemic. The Covid-19 pandemic was able to spread rapidly around the world, indicating that extreme measures were needed to bring it under control. The policies of governments around the world have had a significant negative impact on the economy. Taking into account these factors, the issue of studying the economic behavior of market participants in the conditions of the pandemic was put on the agenda.
 Making real economic decisions based on rationality seems to be extremely difficult for all economic agents under the conditions of the Covid-19 pandemic. Therefore, when economic agents are faced with the extremely difficult situation created by the pandemic, rational economic decisions are likely to become ineffective or inefficient, and therefore the principle of "bounded rationality" described in the field of behavioral economics may become ineffective in the implementation of strategies to mitigate the consequences of the pandemic [15].
 The article discusses the use of the "Nudge Theory" in the public and private sector to implement incentives in economic decision-making.
 
 The purpose of the study: To consider a new economic policy using the approaches of behavioural economics and give recommendations on its development and implementation in the public and private sectors. Studying scientific experiments and articles by leading scientists, adapting the main conclusions, analysing the implications for policy implementation.
 
 Methodology/approach: Analysis of field data and review of laboratory experiments based on a bibliographic review of behavioral economics.
 
 Scientific news: Author's assessments or recommendations.
 
 Practical value: For new courses and disciplines.
 
 Keywords: Behavioral Economics, Economic Policy, "Nudge Theory", Incentives, Economic decision-making.

This paper discusses the concepts of data warehouse technology and its importance for decision support system (DSS). The DSS as one kind smart software system of the important application value, provides, each kind of decision information as well as the commercial question solution for the enterprise. Thus Data warehouse can meet the requirements of the database management subsystems of DSS, and is fitted to form its technology frame. Then we put forth the structure of data warehouse and its main functional components. Along with widespread application of the data warehouse, DSS based on data warehouse arises at the historic moment. Begin with actual demand of the DSS, characteristic and the structure of the data warehouse have been analyzed. DSS based on the data warehouse technology is elaborated, and finally give the application example.

This paper was published in 1999, a few years before Daniel Kahneman and Vernon Smith won the 2002 Nobel Prize in economics. Many of us in the field of behavioral economics suspected that a Nobel Prize was in the works for our field, and we worried that it would be jointly conferred on behavioral economics and experimental economics by people who couldn’t tell the difference between them. The paper starts by pointing out that behavioral economics and experimental economics are entirely different enterprises. Behavioral economics is the application of insights from psychology (and sometimes other disciplines) to economics. Experimental economics is the application of experimental methods to economics.

In economics it has traditionally been assumed that people make all their decisions like the so‐called homo oeconomicus – that is, maximizing (expected) utility of total wealth. In recent years, economics increasingly recognized that people often exhibit behavioral patterns which are incompatible with the idea of the homo oeconomicus. The field of behavioral economics incorporates insights from the field of psychology to explain discrepancies between predictions of traditional economic theory and actual observed behavior. In this paper, we summarize a selection of well‐established behavioral patterns observed in reality and discuss their relevance for the insurance industry when it comes to better understanding and predicting customer behavior. We also explain that people are not always risk‐averse and give a brief overview over Prospect Theory (probably the most popular behavioral economics alternative to Expected Utility Theory), its shortcomings for predicting behavior over a long time horizon, and its extensions. In total, we point out that, since dealing with risks and insurance products requires complex decision making processes, a deep understanding of the impacts of behavioral factors is essential to better assess and explain costumer behavior.

Congestion in Malta is a persistent national problem. In a country which has a land mass of just 316km2, with a total population verging near a million, the major means of transportation is road transport, making it the fifth most densely populated nation and the fifth most dense transport network in the world. In order to alleviate or solve this problem, MRT (Mass Rapid Transport or Transit) is being proposed to make a shift from single private vehicles to mass public transportation. This study adopted a postpositivist quantitative approach, employing a convenience, non-representative stratified sample, and using second generation statistical methods, specifically Partial Least Squares Structural Equation Modelling (PLS-SEM), to test the validity, reliability, and predictability of a proposed Extended Theory of Planned Behavior (ETPB) model, that combined the Theory of Planned Behavior (TPB) and elements of Behavioural Economics (BE), specifically Prospect Theory (PT). The study examines the social, economic, and political barriers faced by licenced drivers, who would obviously be the key element needed in this modal transportation switch. The conclusion that is derived from this study is that is that TPB is significantly intrinsic to the PT, and whilst the inclination of the attitude of Maltese licenced drivers to switch and adopt the use of MRT is weak, there is still a positive inclination.

This thesis contributes to the field of behavioral economics. Research in behavioral economics investigates whether predictions of theoretical models and their underlying assumptions hold. It thereby enhances the predictive power of economics. While the first chapter provides an introduction and overview, Chapter 2 of this thesis studies whether an intergenerational transmission of risk preferences revealed by occupational choice from parents to children can be observed. The analysis shows that different male generations of a family indeed exhibit similar risky behavior, however, the link is rather weak. Chapter 3 answers the question of whether subjects are aware of the correlation between risk preferences and sociodemographic characteristics and attach informational value to sociodemographics when assessing the risk attitudes of others. The results of the experiment show that subjects recognize the correlation between the sociodemographic variables age, gender, parenthood and family status and risk preferences. Furthermore, subjects attach informational value to sociodemographic characteristics. Chapter 4 analyzes the process of the assessment of risk preferences of others in detail. A major asset is that our sample for the experiments in Chapter 3 and 4, besides student subjects, includes individuals that professionally make decisions on behalf of others every day: senior and junior advisors with differing years of counseling experience in the financial sector. In particular, it is studied whether professional and non-professional advisors are capable of correctly assessing the risk preferences of a specific advisee given sociodemographic information. The results of the experiment show, that when forming beliefs about the risk preferences of others, a false consensus bias of the advisors is present; the advisors' own risk preferences positively correlate with their beliefs about the advisees' ones. The precision of advice increases if more information is available. Professionals outperform non-professionals significantly when forecasting the risk attitudes of others. Chapter 5 of this thesis analyses whether herding out of reputational concerns exists in financial markets. Therefore, the subjects' behavior is studied in two treatments. First, in the role of financial investors, subjects take the decision which of two assets to buy without career concerns. In a second treatment, career concerns are incorporated. While in the first treatment, according to the theory, investors should always follow their own signal and buy the respective asset, in the second treatment with career concerns, investors should engage in herd behavior and neglect their own signal. However, the experimental results indicate that a similar herd behavior can be observed in the treatment with reputational concerns as well as in the treatment without reputational concerns. Moreover, we provide further evidence for investors (irrationally) imitating predecessors.

The conceptualization of environmental policies aiming to promote pro-environmental behavior has long been based mainly upon the theoretical inputs provided by standard economics. As a result of a growing awareness about the limited effectiveness of such concepts there is to be observed a re-orientation of environmental policy toward “soft-policy” instruments. In this connection, social scientists and policymakers have increasingly given attention to behavioral economics as a basis for designing more effective policy instruments. Research concerning this approach and especially its actual application are, however, still at an early stage, and, moreover, the field of behavioral economics is still developing.

Nudging has emerged as an alternative policy for managing water demand in the residential sector. Indeed, numerous field studies have been recently published to assess the impact of nudges on water consumption. In parallel, a substantial body of literature has developed in the field of behavioral economics, including laboratory experiments to evaluate the effects of nudge-type or boost treatments on individual behavior. However, the corresponding results are frequently overlooked in field studies. In this context, focusing on the residential water sector, the aim of this survey paper are, first, to review field experiments and address key issues in behavioral economics; and second, to present results obtained from laboratory experiments that could enhance nudging policies. We conclude by discussing additional unexplored areas and their policy implications.

BackgroundInvestment decision making is a critical aspect of financial planning. It involves allocating the financial resources to various investment avenues with an objective of generating future returns. Behavioral finance provides a theoretical framework for understanding psychological biases investment decision making which changes the assumptions of traditional finance. This study examines five important behavioral biases such as heuristics, prospect theory, emotions, market impact, and herding behavior on investment decision-making and portfolio management by considering investor experience and financial literacy as moderating factors.MethodsPrimary data was collected from individual investors in the BSE and NSE using a structured questionnaire administered through a purposive random sampling. Resulting in 151 complete responses were obtained and were considered valid for the purpose of our study. PLS-SEM was used to test the proposed hypotheses as well as the moderating effectResultsStudy finding indicates that heuristics have a positive and statistically significant effect on investment decisions, while prospect theory, emotions, market impact, and herding behavior showed no significant direct influence. The moderation analysis reveals that both investor experience and financial literacy significantly moderate the effects of emotions and market impact on investment decisions. However, their influence on heuristics, prospect theory, and herding behavior was statistically insignificant.ConclusionsThe results of this study lead to several conclusions. In this present study only one behavioral bias heuristics (HU) demonstrated a significant direct influence on investment decisions. In contrast, other behavioral biases such as prospect theory (P), emotions (E), market impact (M), and herding behavior (HB) do not significantly affect investment decisions (p > 0.05). The moderating effect of investors’ experience and financial literacy investor behavior are minimal. The result underscores that improving financial education, skills, knowledge and gaining experience may help investors regulate emotional and trend-based decisions but may not be sufficient to address more instinctive cognitive biases. The significance of the study provides important implications for financial educators, advisors, policymakers and stock market authorities regarding the need for behaviorally informed investor training, decision-support systems, and informed advisory services to promote rational investment behavior.