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The usage of machine learning methods in the financial sector, regarding repayment prediction or forecasting, is quite a new topic, constantly gaining in importance. The concept of the alternative costs in the literature covering machine learning and deep learning occurs most often in connection with the non-financial areas as costs of lost benefits. This empirical paper presents research dedicated to deep learning used in forecasting the alternative costs of leasing represented by the variable KUK_PRC. The study is based on the experimental approach and uses real organization data to solve the forecasting problems in the financial area with AI solutions. This research contributes to the science by identifying and exploration of the research gap in the field of applied economics and finances. The main finding of this paper is the proposed forecasting ACSeq-DNN model that forecasts opportunity costs with smaller deviations from actual values than the forecasting achieved by state-of-the-art models. [ABSTRACT FROM AUTHOR]

This paper delivers up-to-date bibliometric research dealing with deep learning used in forecasting alternative costs in leasing. The study presents a theoretical approach that is developed by the bibliometric maps. The aim of this paper is the identification and exploration of the research gap. The study illustrates the relationships between different science areas, such as applied economics and finances, computer science, deep learning, and machine learning. The alternative costs in the literature are related to non-financial areas and the other Artificial Intelligence (AI) methods to calculate them. There is a scarcity of alternative costs calculation with deep learning procedures or their theoretical analysis. To fill this gap, this study uses VOSviewer software to explore queries' results in the Scopus database and to illustrate the knowledge about alternative costs in deep or machine learning. The results show that: (I) the number of publications tends to increase gradually; (II) prediction or forecasting of the alternative costs is connected directly neither to the lost benefits, nor costs and risk assessment; (III) most studies concern the use of statistical methods in forecasting, but only a few use machine learning algorithms or decision trees. This scientific paper serves as a starting point for future studies dedicated to the deep learning procedures to forecast alternative costs in leasing or other financial processes. [ABSTRACT FROM AUTHOR]

This paper examines some of the important trends in economics that influenced the liberalization of aviation markets from the late 1970s, the role that economics has played in the subsequent assessment of the implications of these reforms and more recent policy "tweaking", and at the possible importance of more recent trends in economic thinking in influencing future policy developments. It primarily highlights the roles played by economic analysis in understanding how markets for airline, airport, and air traffic control work, how many of these features have been quantified, and situations where serious market failures can occur. • Provides assessment of economic theory in developing aviation policy. • Provides a contextual overview of the ways in which economic regulation develops. • Offers some thoughts about the future role of economics in shaping aviation policy. [ABSTRACT FROM AUTHOR]

The increasing physical and economic scarcity of water due to increasing societal demands and climate change will require worldwide water policy reform. Water reform is an area of public policy fraught with polarised positions regarding community and environmental welfare. As opposition to water policy reform becomes entrenched, transaction costs increase. Nowhere is this more evident than the controversy surrounding, and irrigators’ opposition to, the Murray-Darling Basin Plan in Australia. This study sought to understand irrigators’ trust issues and why they feel the way they do towards water reform, though a best-worst survey methodology and regression analysis. The results suggest that irrigators believe they are shouldering a fair share of the water reform burden. Lack of trust in the national water agency and the federal government is associated with irrigator location, age and climate change disbelief. Findings support the recent push for more localised water decision-making to promote social trust. [ABSTRACT FROM AUTHOR]

Published choice experiments linked to various aspects of the COVID-19 pandemic are analysed in a rapid review. The aim is to (i) document the diversity of topics as well as their temporal and geographical patterns of emergence, (ii) compare various elements of design quality across different sectors of applied economics, and (iii) identify potential signs of convergent validity across findings of comparable experiments. Of the N = 43 published choice experiments during the first two years of the pandemic, the majority identifies with health applications (n = 30), followed by transport-related applications (n = 10). Nearly 100,000 people across the world responded to pandemic-related discrete choice surveys. Within health applications, while the dominant theme, up until June 2020, was lockdown relaxation and tracing measures, the focus shifted abruptly to vaccine preference since then. Geographical origins of the health surveys were not diverse. Nearly 50% of all health surveys were conducted in only three countries, namely US, China and The Netherlands. Health applications exhibited stronger pre-testing and larger sample sizes compared to transport applications. Limited signs of convergent validity were identifiable. Within some applications, issues of temporal instability as well as hypothetical bias attributable to social desirability, protest response or policy consequentiality seemed likely to have affected the findings. Nevertheless, very few of the experiments implemented measures of hypothetical bias mitigation and those were limited to health studies. Our main conclusion is that swift administration of pandemic-related choice experiments has overall resulted in certain degrees of compromise in study quality, but this has been more so the case in relation to transport topics than health topics. • Discrete choice experiments conducted in the context of COVID-19 are analysed and evaluated. • Major discrepancies observed between health and transport sectors in terms of survey quality. • Health studies used larger samples, employed measures of hypothetical bias mitigation more often. • Health studies reported more often on use of piloting and measures of internal validity testing. • Use of social media as recruitment platform was far more prevalent in transport surveys. [ABSTRACT FROM AUTHOR]

Behavioral economics is an approach to understanding behavior though integrating behavioral psychology and microeconomic principles. Advances in behavioral economics have resulted in quick-to-administer tasks to assess discounting (i.e., decrements in the subjective value of a commodity due to delayed or probabilistic receipt) and demand (i.e., effort exerted to defend baseline consumption of a commodity amidst increasing constraints)—these tasks are built upon decades of foundational work from the experimental analysis of behavior and exhibit adequate psychometric properties. We propose that the behavioral economic approach is particularly well suited, then, for experimentally evaluating potential public policy decisions, particularly during urgent times or crises. Using examples from our collaborations (e.g., cannabis legalization, happy hour alcohol pricing, severe weather alerts, COVID-19 vaccine marketing), we demonstrate how behavioral economic approaches have rendered novel insights to guide policy development and garnered widespread attention outside of academia. We conclude with implications on multidisciplinary work and other areas in need of behavioral economic investigations. • Behavioral economics incorporates discounting and demand. • Demand and discounting have historically provided novel information for health issues. • Behavioral economics is uniquely situated to empirically inform policies. [ABSTRACT FROM AUTHOR]

This paper follows the review of empirical evidence on the existence of hypothetical bias (HB) in choice experiments (CEs) presented in Part I of this study. It observes how the variation in operational definitions of HB has prohibited consistent measurement of HB in CE. It offers a unifying definition of HB and presents an integrative framework of how HB relates to but is also distinct from external validity (EV), with HB representing one component of the wider concept of EV. The paper further identifies major sources of HB and discusses explanations as well as possible moderating factors of HB. The paper reviews methods of HB mitigation identified in the literature and the empirical evidence of their effectiveness. The review includes both ex-ante and ex-post bias mitigation methods. Ex-ante bias mitigation methods include cheap talk , real talk , consequentiality scripts , solemn oath scripts , opt-out reminders , budget reminders , honesty priming , induced truth telling , indirect questioning , time to think and pivot designs. Ex-post methods include follow-up certainty calibration scales , respondent perceived consequentiality scales , and revealed-preference-assisted estimation. It is observed that the mitigation methods and their preferred use vary markedly across different sectors of applied economics. The existing empirical evidence points to the overall effectiveness of mitigation strategies in reducing HB, although there is some variation. The paper further discusses how each mitigation method can counter a certain subset of HB sources. Considering the prevalence of HB in CEs and the effectiveness of bias mitigation methods, it is recommended that implementation of at least one bias mitigation method (or a suitable combination where possible) becomes standard practice in conducting CEs to ensure that inferences and subsequent policy decisions are as much as possible free of HB. • Definition and conceptualisation of hypothetical bias in choice experiments is revisited. • The relationship between hypothetical bias and external validity in choice experiments revisited. • Sources/explanations and moderating factors of hypothetical bias are identified. • Methods of hypothetical bias mitigation and the empirical evidence on their effectiveness reviewed. • Recommendations offered for adopting mitigation methods tailored to likely source(s) of hypothetical bias. [ABSTRACT FROM AUTHOR]

The notion of hypothetical bias (HB) constitutes, arguably, the most fundamental issue in relation to the use of hypothetical survey methods. Whether or to what extent choices of survey participants and subsequent inferred estimates translate to real-world settings continues to be debated. While HB has been extensively studied in the broader context of contingent valuation, it is much less understood in relation to choice experiments (CE). While the number of CE studies has rapidly increased, the critical issue of HB has been studied in only a small fraction of CE studies. This paper provides macro-scale insights into the literature of CE and reviews the empirical evidence for HB in CE in various fields of applied economics as well as experimental psychology and behavioural neuroscience. Results suggest mixed evidence on the prevalence, extent and direction of HB as well as considerable context and measurement dependency. While HB is found to be an undeniable issue when conducting CEs, the empirical evidence on HB does not render CEs unable to represent real-world preferences. While health-related choice experiments often find negligible degrees of HB, experiments in consumer behaviour and transport domains suggest significant degrees of HB. Environmental valuation studies provide mixed evidence. Also, across these disciplines many studies display HB in their total willingness to pay estimates and opt-in rates but not in their hypothetical marginal rates of substitution (subject to scale correction). Further, recent findings in psychology and brain imaging studies identify neurocognitive mechanisms underlying HB that may explain some of the discrepancies and unexpected findings in the mainstream CE literature. • Empirical evidence on the prevalence of hypothetical bias in choice experiments is reviewed. • The survey integrates findings of consumer, health, environmental and transport economics. • Evidence from experimental psychology partly explain mechanisms of hypothetical bias. • Hypothetical bias is found to be an undeniable issue in relation to choice experiments. • The prevalence of significant bias varies considerably across disciplines and contexts of choice. [ABSTRACT FROM AUTHOR]