Real-world policy decisions involve trade-offs. Sometimes the trade-offs involve both the efficacy and morality of potential policies. In this chapter, the morality and likely efficacy of hiring one more spy to help anti-terrorist intelligence gathering efforts is compared to the morality and likely efficacy of implementing a prediction market on terrorism. Prediction markets on terrorism allow registered traders to buy and sell shares in predictions about terrorism-related real-world events. The comparison at the heart of this chapter is (...) based on the assumption that it would cost about $5 million to bankroll the prediction market project, or to establish another spy, including equipment and head office support, for 15 years. The comparison reveals that implementing a prediction market on terrorism is likely to be more efficacious and less morally problematic than hiring one more spy. (shrink)

This essay argues that prediction markets, as one approach for aggregating dispersed private information, may not only be praised for their epistemic accuracy. They also feature characteristics that are morally desirable from the point of view of epistemic justice. Notably, they are a promising approach when we are trying to address testimonial injustice. The impersonality of market transactions effectively tackles the issue of identity prejudice, which underlies many forms of testimonial injustice. This is not to say that (...) class='Hi'>prediction markets do not pose challenges for epistemic justice of their own like the risk of excluding knowers based on ability to pay. Or, in light of identity prejudice’s systematicity, the risk of discouraging some knowers because of differences in education or social background that lead to a certain unease when faced with prediction markets. The essay discusses these challenges, concedes limitations but also offers countermeasures to certain worries – e.g. hosting prediction markets on a play-money basis. Throughout, the essay addresses the question of the potential scope of using prediction markets for epistemic purposes, notably by means of the practical example of a prediction market for monetary policy. (shrink)

We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. (...) Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting is not a WPBE. In the infinite-stage model with unconditionally independent signals, there does not exist an equilibrium in which all information is revealed in a finite amount of time. We propose a simple discounted market scoring rule that reduces the opportunity for bluffing strategies. We show that in any WPBE for.. (shrink)

Prediction markets, which trade contracts based on the results of predictions, have been remarkably successful in predicting the results of political events. A number of proposals have been made to extend prediction markets to scientific questions, and some small-scale science prediction markets have been implemented. Advocates for science prediction markets argue that they could alleviate problems in science such as bias in peer review and epistemically unjustified consensus. I argue that bias in (...) peer review and epistemically unjustified consensuses are genuine problems in science, and that current attempts to solve them fail in practical circumstances. Prediction markets do show some promise for answering scientific questions while avoiding the pitfalls of consensus and peer review. However, there are strong reasons to believe that science prediction markets will not perform nearly so well as election prediction markets, and implementing prediction markets on a large scale could have significant unintended consequences for the organization of scientific research. The promise of prediction markets to solve current problems in assessing scientific claims is therefore largely illusory. (shrink)

Prediction markets are low volume speculative markets whose prices offer informative forecasts on particular policy topics. Observers worry that traders may attempt to mislead decision makers by manipulating prices. We adapt a Kyle-style market microstructure model to this case, adding a manipulator with an additional quadratic preference regarding the price. In this model, when other traders are uncertain about the manipulator’s target price, the mean target price has no effect on prices, and increases in the variance of (...) the target price can increase average price accuracy, by increasing the returns to informed trading and thereby incentives for traders to become informed. (shrink)

The quest to foretell the future is omnipresent in human affairs. A potential solution to this epistemological conundrum has emerged through mass collaboration. Motored by the Internet, prediction markets allow a multitude of individuals to assume a stake in a security whose value is tied to a future event. The resulting prices offer a continuously updated probability estimate of the event actually taking place. This paper gives a survey of prediction markets, their history, mechanics, uses, and (...) theoretical foundation. We also review the literature surrounding their efficacy. Though there are shortcomings with prediction markets, as well as practical constraints, they hold out the prospect of improving the quality of organizational decisions and increasing the level of participation in the deliberative process. We also note that lessons can be drawn from these markets to guide the epistemological practices of disciplines and inquiries in which empirical methods are difficult to apply. (shrink)

In this paper, prediction markets that encourage traders to bet on matters of life and death are used to explore the varieties and dynamics of moral repugnance. We define moral repugnance as morally charged feelings of revulsion that correspond (correctly, incorrectly, and indeterminately) to moral reasons and contexts. Rich variations of moral repugnance and their dynamic qualities are presented by investigating the contextual frames in which they arise. These contextual frames constitute interacting conditions composed of information about states (...) of affairs, moral reasons, and feelings of revulsion. Through careful study of two medical prediction markets that encourage betting on death, we can observe the interaction between these causal conditions to see how the varieties of moral repugnance emerge. We also present three interesting results that arise from analyzing the dynamics of moral repugnance in response to prediction markets. First, a prediction market can elicit several conflicting types of moral repugnance at the same time. Second, moral indeterminacy can arise in two different ways when judging prediction markets. Finally, some prediction markets can generate a moral endogeneity problem, a disruptive feedback loop between a given prediction market and the morally relevant outcome it predicts. (shrink)

Predicting terrorist attacks with prediction markets has been accused of being immoral. While some of these concerns are about the likely effectiveness of prediction markets on terrorism (PMsoT), this paper discusses the three main reasons why even effective prediction markets on terrorism might be considered immoral. We argue that these three reasons establish only that PMsoT cause offense and/or fleeting mild harm, and that, even taken together, they do not constitute serious harm. The moral (...) issues considered are that PMsoT: 1) create character-affecting perverse incentives, 2) desensitise society to tragic events, and 3) disrespect important ideals. In addition to arguing against the force of these three potential moral problems, it is also argued that societies and governments already endorse intelligence-gathering methods that are clearly more immoral than PMsoT in the relevant respects. We also argue that some circumstances require governments to cause non-serious harm to some people in order to protect and promote the rights and welfare of its citizens. We conclude that a government’s obligation to protect and promote the rights and welfare of its citizens outweighs the non-serious harm that could be caused by effective PMsoT. As a result, we recommend that the likelihood of PMsoT being effective is investigated more closely. (shrink)

For multiple reasons, deliberating groups often converge on falsehood rather than truth. Individual errors may be amplifi ed rather than cured. Group members may fall victim to a bad cascade, either informational or reputational. Deliberators may emphasize shared information at the expense of uniquely held information. Finally, group polarization may lead even rational people to unjustifi ed extremism. By contrast, prediction markets often produce accurate results, because they create strong incentives for revelation of privately held knowledge and succeed (...) in aggregating widely dispersed information. The success of prediction markets off ers a set of lessons for increasing the likelihood that groups can obtain the information that their members have. (shrink)

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose (...) to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity. (shrink)

Predicting stock markets is a problem that has generated many answers. According to one group of responses, the divergence thesis, it is impossible to accomplish this since the prediction has a ‘bending effect’ that would cause the market to behave in a way that would permanently depart from what was predicted, i.e. the prediction would falsify itself. There are at least three types of impossibility: logical, theoretical and empirical. A second class of responses argues that despite the (...) ‘bending effect’ of predictions, it is still feasible to predicting stock markets. These responses, the convergence thesis, contend that we can achieve it by demonstrating that there are fixed points or that the prediction and market behavior will eventually converge. I expand this line of reasoning by showing that the performativity makes it possible certain predictions by an alignment between the ‘ontic’ and the ‘epistemic’ state of the markets. In addition, I show that performativity enables us to explain how a prediction is produced, why it works initially and then why it fails (i.e. why its predictive power is destroyed). (shrink)

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron, recurrent neural network, and autoregressive integrated moving average on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock (...) market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting. (shrink)

This paper addresses the recent rise of the use of alternative medicine in Western countries. It offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory theory defended (...) here is then predicated on the idea that an extreme patient-centric model of medical practice that treats largely ignorant patients as consumers of medical products and services endowed with an essentially unrestricted power of freedom to choose treatments predictably leads serious and avoidable harms. Some important moral and epistemological consequences of this model are then articulated and corrective measures are suggested. (shrink)

As a brand-new marketing method, network marketing has gradually become one of the main ways and means for enterprises to improve profitability and competitiveness with its unique advantages. Using these marketing data to build a model can dig out useful information that the business is concerned about, and the company can then formulate marketing strategies based on this information. Sales forecasting is to speculate on the future based on historical sales. It is a tool for companies to determine production volume (...) and ensure the balance of product supply and sales. It can help companies make correct business decisions to maximize profits. The neural network can approximate the nonlinear function with arbitrary precision, and the time series prediction model based on the neural network can well reflect the nonlinear development trend of information. Based on the analysis of the shortcomings of the traditional BP network, this paper uses a genetic algorithm with good global search capabilities to improve the neural network. The thought and theory of optimizing the initial weight and threshold of the neural network of the GA algorithm are discussed in detail. While expounding the forecasting method, it uses specific examples to analyze the performance and characteristics of the GA-BP network in the enterprise network marketing forecasting. The results show that the GA-BP neural network is higher than the traditional BP neural network in terms of prediction accuracy and adaptability. (shrink)

It is frequently argued that false and misleading claims, spread primarily on social media, are a serious problem in need of urgent response. Current strategies to address the problem – relying on fact-checks, source labeling, limits on the visibility of certain claims, and, ultimately, content removals – face two serious shortcomings: they are ineffective and biased. Consequently, it is reasonable to want to seek alternatives. This paper provides one: to address the problems with misinformation, social media platforms should abandon third-party (...) fact-checks and rely instead on user-driven prediction markets. This solution is likely less biased and more effective than currently implemented alternatives and, therefore, constitutes a superior way of tackling misinformation. (shrink)

This study investigates the volatility risk premium on the emerging financial market. We also consider the expected return and ESG sentiment. Based on the SSE 50 ETF 5-minute high-frequency spots and daily options data from 2016 to 2021, we adopt nonparametric model-free approaches to calculate realized and implied volatilities. And the volatility risk premium is constructed by subtracting these volatility series. We examine the relations between the volatility risk premium and future excess returns as well as ESG sentiment through multifactor (...) specifications. We find that the volatility risk premium also exists in the Chinese market and is significantly negative. In addition, the statistically positive correlation between the volatility risk premium and aggregate returns is an outlier compared to the empirically negative pattern in developed markets. At last, ESG sentiment is positively associated with the volatility risk premium, especially the impact of environmental and social. This evidence supports the agency theory, which indicates that investors perceive ESG investments as waste resources in a short term and become potentially risky. (shrink)

Marketing research interviewers often feel that they must compromise their own moral principles while executing work-related activities. This finding is based on analysis of data obtained from three focus group interviews and a mail survey of 173 telephone survey interviewers. Data from the mail survey were used to construct scales measuring interviewers' perceived necessity of moral compromise, moral character, and job satisfaction. The three scales then were used in a hierarchical regression analysis to predict incidences of interviewers' self-reported proscribed behaviors (...) on the job, the latter being an index of behaviors known by interviewers to be wrong. Results support all hypothesized relationships. (shrink)

It is frequently argued that false and misleading claims, spread primarily on social media, are a serious problem in need of urgent response. Current strategies to address the problem – relying on fact-checks, source labeling, limits on the visibility of certain claims, and, ultimately, content removals – face two serious shortcomings: they are ineffective and biased. Consequently, it is reasonable to want to seek alternatives. This paper provides one: to address the problems with misinformation, social media platforms should abandon third-party (...) fact-checks and rely instead on user-driven prediction markets. This solution is likely less biased and more effective than currently implemented alternatives and, therefore, constitutes a superior way of tackling misinformation. (shrink)

Prediction markets are increasingly being considered as methods for gathering, summarizing and aggregating diffuse information by governments and businesses alike. Critics worry that these markets are susceptible to price manipulation by agents who wish to distort decision making. We study the effect of manipulators on an experimental market, and find that manipulators are unable to distort price accuracy. Subjects without manipulation incentives compensate for the bias in offers from manipulators by setting a different threshold at which they (...) are willing to accept trades. (shrink)

The inconsistency between pro-environmental attitudes and behaviours, known as the “attitude-behaviour” gap, is exceptionally pronounced in scenarios associated with “green” choice. The current literature offers numerous explanations for the reasons behind the “attitude-behaviour” gap, however, the generalisability of these explanations is complex. In addition, the answer to the question of whether the gap occurs between attitudes and intentions, or intentions and behaviours is also unknown. In this study, we propose the moral dimension as a generalisable driver of the “attitude-behaviour” gap (...) and investigate its effectiveness in predicting attitudes, pro-environmental intentions and subsequent behaviours. We do so by using Hunt–Vitell’s moral philosophy-based framework of ethical decision-making, which conceptualises morality as the central decision-making parameter. The results from 557 US MTurk participants revealed that the manipulation of moral dimensions, specifically deontology and teleology, impacted ethical evaluation of presented dilemmas, however, failed to translate into subsequent intentions and behaviours. This finding suggests (i) that the moral dimension has an effect in shaping attitudes toward environmental issues, and (ii) that gap occurs between attitudes and intentions rather than intentions and behaviours. Further investigation of what strengthens and/or overrides the effects of the moral dimension would help understand the reasons why moral attitudes do not always translate into subsequent intentions and behaviours in the pro-environmental domain. (shrink)

With the progress of social production, the competition for talents among enterprises is fierce, and the market often lacks capable leaders, which leads to the lack of management of enterprise employees and cannot bring more economic benefits to enterprises. Traditional leaders make subordinate employees work actively and achieve the common goal of the enterprise by exerting their own leadership characteristics and observing their subordinates, but they cannot take care of the psychological state of each employee, resulting in the employee's work (...) efficiency is not very high. In recent years, charismatic leadership has become an important economic leader in the new era, and the theoretical spirit of charismatic leadership can well guide employees to work actively. Artificial intelligence affective computing can well identify the psychological state of the subject, and the combination of artificial intelligence affective computing and charismatic leadership can achieve effective management of employees through the predictive analysis of employees' psychological state. This paper compares the psychological state prediction analysis of employees' work attitudes between charismatic leaders based on artificial intelligence affective computing and traditional leaders through experiments. The results show that: charismatic leaders based on artificial intelligence affective computing predictive analytics can improve sensitivity to employee needs, resulting in an 8.2% increase in employee trust in leadership, a 4.4% increase in employee commitment to achieving organizational goals, and a 19.3% increase in employee job satisfaction. The psychological state prediction analysis of charismatic leaders based on artificial intelligence affective computing on employees' work attitudes can improve the work efficiency of employees and the economic benefits of enterprises. (shrink)

The efficient markets hypothesis has been the central proposition in finance for nearly thirty years. It states that securities prices in financial markets must equal fundamental values, either because all investors are rational or because arbitrage eliminates pricing anomalies. This book describes an alternative approach to the study of financial markets: behavioral finance. This approach starts with an observation that the assumptions of investor rationality and perfect arbitrage are overwhelmingly contradicted by both psychological and institutional evidence. In (...) actual financial markets, less than fully rational investors trade against arbitrageurs whose resources are limited by risk aversion, short horizons, and agency problems. The book presents and empirically evaluates models of such inefficient markets. Behavioral finance models both explain the available financial data better than does the efficient markets hypothesis and generate new empirical predictions. These models can account for such anomalies as the superior performance of value stocks, the closed end fund puzzle, the high returns on stocks included in market indices, the persistence of stock price bubbles, and even the collapse of several well-known hedge funds in 1998. By summarizing and expanding the research in behavioral finance, the book builds a new theoretical and empirical foundation for the economic analysis of real-world markets. (shrink)

This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression (...) analyses are commonly seen as necessary in order to identify aggregate-level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds of prediction necessary and possible in spatial planning of urban development. Finally, the political implications of positions within theory of science rejecting the possibility of predictions about social phenomena are addressed. (shrink)

Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can come to (...) be adequate for purposes that are defined by both their guiding research questions and their larger social context of application. (shrink)

Managers conduct open market repurchases for many different reasons, including to distribute excess cash. However, the most widely discussed explanation for OMRs is the “signaling theory:” that managers announce OMRs to signal that the stock is underpriced. The first purpose of this paper is to show that the signaling theory is theoretically problematic—in part because it assumes managers deliberately sacrifice their own wealth to increase that of shareholders—as well as inconsistent with much of the empirical evidence. The second purpose of (...) the paper is to put forward an alternative explanation for managers’ use of OMRs: the managerial-opportunism theory. This theory, which assumes that managers seek to maximize their own wealth, predicts that managers announce OMRs both when the stock is underpriced and when it is not. When the stock is underpriced, managers may announce and conduct an OMR to transfer value to themselves and other remaining shareholders. When managers wish to sell a large portion of their shares, they announce an OMR to boost the stock price before selling their shares. The paper shows that managerial opportunism is not only a more plausible motive for OMRs than is signaling, it is also more consistent with the empirical data. The paper concludes by describing some testable predictions of the theory. (shrink)

This book is about the mind and its connection to the brain. The first two chapters discuss the basic characteristics of the mind, and places it in historical context by noting trends in popular culture, and various people's ideas about the mind. This discussion ends by concluding that the most fruitful approach to studying the mind is a scientific approach that looks for connections between the mind and the brain. The last four chapters focus on the following specific principles: The (...) operation of the mind is hidden; Prediction is an important function of the mind; and Connectivity and communication between different areas of the mind are crucial for the mind's operation. An important feature of this discussion is that it is supported by empirical research, by briefly describing experiments. This book therefore goes beyond simply presenting conclusions, by elucidating the procedures and reasoning that led to those conclusions. The intent of the book is to give readers a feel for the latest scientific thinking about the mind, by focusing on basic principles that hold across many different functions of the mind. One key to marketing the book is to emphasize the fact that the writing makes a fascinating, but complex, subject accessible to the general reader. (shrink)

Prediction markets designed to predict terrorism through traders’ investments on the likelihood of specific terrorist attacks are, strictly speaking, enabling those traders to bet on terrorism. Betting on terrorist attacks, like some other forms of betting on death, has been accused of being repugnant. In this paper, it is argued that while government-backed effective intelligence-gathering prediction markets on terrorism (PMsoT) might elicit feelings of repugnance, those feelings are likely to be misguided. The feelings of repugnance arise (...) because PMsoT are assumed to be associated with terrorism in encouraging and endorsing ways and with human death in a disrespectful way. However, it is argued that these feelings of repugnance are misguided in the case of government-backed effective intelligence-gathering PMsoT because the purpose and effect of such prediction markets, and (on balance) the trading they encourage, is to prevent and renounce terrorism and show respect for matters of life and death. (shrink)

In the 1970s, experimental economics split from social psychology by embracing rational choice and equilibrium methods. Behavioral economics has recently narrowed the divide, to the dismay of some. The present paper argues that evolutionary dynamics provides a framework which unifies the best features of social psychology with equilibrium and rational choice. Ongoing research in cheating in markets illustrates the main points. A new equilibrium model provides distinctive testable predictions under three regimes: autarky, frictionless free trade, and anonymous foreign trade (...) with opportunities to cheat. The predictions organize quite well the data collected so far. Later phases of the project will allow trader networks to evolve, altering the market institution and perhaps affecting preferences. Thus the ma jor forces recognized by social psychologists can be combined with a rationality and equilibrium to study how markets respond to the risk of cheating. (shrink)

Telecommunications is one of the most dynamic sectors in the market, where the customer base is an important pawn in receive safe revenues, so is important to focus attention is paid to maintaining them with an active status. Migrating customers from one network to another varies among telecommunication companies depending on different factors such as call quality, pricing plan, minute consumption, data, sms facilities, customer billing issues, etc. Determining an effective predictive model helps detect early warning signals when churn occurs (...) and assigns to each customer a score called “churn score” that indicates the likelihood that the individual might migrate to another network over a predefined time period. To this extent, the present paper uses more than 10k customers sample of a telecommunication company and tries to analyse the churn behavior. The aim of the paper is both to test the efficiency and performance of the most commonly used data mining techniques to predict the churn behavior and to underline the main indicators that can be used when conducting such analyses. Knowing the magnitude of the churn phenomenon, the company can prevent the instability that is going to occur by applying a series of measure in order to increase the retention of the current customers. (shrink)

A legal system is any institution or set of institutions in a given society that provides dispute resolution in a systematic and reasonably predictable way. it does so through the exercise of three functions: the judicial, the legislative, and the executive. The judicial function, the adjudication of disputes, is the core of any legal system; the other two are ancillary to this. The legislative function is to determine the rules that will govern the process of adjudication (this function may be (...) merged with the judicial function, as when case law arises through precedents, or it may be exercised separately), while the executive function is to secure submission (through a variety of means, which may or may not include violence) to the adjudicative process and compliance with its verdicts. A government or state (for present purposes i shall use these terms interchangeably) is any organisation that claims, and in large part achieves, a forcibly maintained monopoly, within a given geographical territory, of these legal functions, and in particular of the use of force in the executive function. now the market anarchist objection to government is simply a logical extension of the standard libertarian objection to coercive monopolies in general.1 First, from a moral point of view, among people regarded as equals2 it cannot be legitimate for some to claim a certain line of work as their own privileged preserve from which others are to be forcibly excluded; we no longer believe in the divine right of kings, and on no other basis could such inequality of rights be justified. Second, from an economic point of view, because monopolies are insulated from market competition and hold their customers by force, they lack both the information and the incentive to provide consumers with fair, efficient, and inexpensive service. The anarchist accepts these arguments, and merely asks why they should apply with any less force to the provision of legal services. (shrink)

The intentional stance1 and the contextual stance2 are inextricably interdependent in the production of a comprehensive explanation and means of predicting complex human behaviour. This is illustrated in the context of the expectation of attitudinal-behavioural consistency which has long lain at the heart of both marketing science and social psychology. In practice, cognitively-inclined attitude theory and research leans on the contextual stance in order to formulate the heuristic overlay of mental interpretation in which it primarily presents its predictive and explicative (...) accounts of behaviour. Behaviour analysis has traditionally eschewed this approach, maintaining that it can generate an exclusively extensional account of complex behaviour. It is argued that while the cognitive and behaviour analytic approaches produce equally effective predictions of behaviour, an adequate explanation of human activity requires the addition of the kind of interpretive overlay advocated by Dennett3 in which the relationship between extensional science and intensionalistic interpretation is clarified. The resulting framework of analysis, intentional behaviorism4 provides an inclusive paradigm. (shrink)

. Prediction of crop yield is significant in order to accurately meet market requirements and proper administration of agricultural activities directed towards enhancement in yield. Several parameters such as weather, pests, biophysical and physio morphological features merit their consideration while determining the yield. However, these parameters are uncertain in their nature, thus making the determined amount of yield to be approximate. It is exactly here that the fuzzy logic comes into play. This paper elaborates an attempt to develop fuzzy (...) inference systems for crop yield prediction. Physio morphological features of Sorghum were considered. A huge database of physio morphological features such as days of 50 percent flowering, dead heart percentage, plant height, panicle length, panicle weight and number of primaries and the corresponding yield were considered for the development of the model. In order to find out the sensitivity of parameters, one-to-one, two-to-one and three-to-one combinations of input and output were considered. The results have clearly shown that panicle length contributes for the yield as the lone parameter with almost one-to-one matching between predicted yield and actual value while panicle length and panicle weight in combination seemed to play a decisive role in contributing for the yield with the prediction accuracy reflected by very low RMS value. (shrink)

Motivated by a large literature on how firm-specific resources drive firm performance, we propose and find that heterogeneity in investor optimism regarding firm-specific attributes plays a very important role in influencing the managerial propensity to manipulate financial statements. When firm-level investor optimism is moderate, the incidence of accounting misconduct increases, but it decreases when investors are highly optimistic. Further, market reaction to the announcement of financial restatements is more negative when investors held more optimistic firm-specific beliefs at the time of (...) initial misstatement. These findings are robust to alternative firm-specific optimism measures linked to analysts, general investors and unsophisticated individual investors, controls for market-wide consumer sentiment unexplained by macroeconomic factors, economy-wide and industry-level optimism, potential selection bias and reverse causality. Our analysis highlights the importance of firm-level investor optimism in predicting, preventing and detecting accounting misconduct. (shrink)

Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to (...) solve the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value. (shrink)

With the increasing use of mobile devices and new technologies, electronic payments, such as near field communication mobile payments, are gaining traction and gradually replacing the currency-based cash payment methods. Despite multiple initiatives by various parties to encourage mobile payments, adoption rates in developing countries have remained low. The purpose of this research is to explore the prime determinants of NFC mobile-payment adoption intention and to develop a model of mobile payment adoption that includes perceived risk as one of the (...) major elements by extending the UTAUT2 theory components. An online survey was used to acquire data from 370 NFC mobile payments users for the current study. To validate the components and their correlations, structural equation modelling was implemented. According to the findings, performance expectancy, hedonic motivation, social influence, and facilitating conditions have substantial impacts on the consumers’ intentions to adopt NFC mobile payments. Effort expectancy and PR were reported to have no considerable effects on the adoption intention. In addition, INFC is revealed to be a major mediator between the associations of the actual adoption of NFC mobile payment with PE, HM, and SI. The findings of the study would assist providers and marketers in better understanding of the consumers’ behavior, designing effective marketing strategies to enhance the consumers’ positive intentions, and achieving the mass adoption of NFC mobile payments in different environmental contexts. (shrink)

While a simple information market lets one trade on the probability of each value of a single variable, a full combinatorial information market lets one trade on any combination of values of a set of variables, including any conditional or joint probability. In laboratory experiments, we compare the accuracy of simple markets, two kinds of combinatorial markets, a call market and a market maker, isolated individuals who report to a scoring rule, and two ways to combine those individual (...) reports into a group prediction. We consider two environments with asymmetric information on sparsely correlated binary variables, one with three subjects and three variables, and the other with six subjects and eight variables (and so 256 states). (shrink)

Beliefs like the Gambler's Fallacy and the Hot Hand have interested cognitive scientists, economists, and philosophers for centuries. We propose that these judgment patterns arise from the observer's mental models of the sequence-generating mechanism, moderated by the strength of belief in an a priori base rate. In six behavioral experiments, participants observed one of three mechanisms generating sequences of eight binary events: a random mechanical device, an intentional goal-directed actor, and a financial market. We systematically manipulated participants’ beliefs about the (...) base rate probabilities at which different outcomes were generated by each mechanism. Participants judged 18 sequences of outcomes produced by a mechanism with either an unknown base rate, a specified distribution of three equiprobable base rates, or a precise, fixed base rate. Six target sequences ended in streaks of between two and seven identical outcomes. The most common predictions for subsequent events were best described as pragmatic belief updating, expressed as an increasingly strong expectation that a streak of identical signals would repeat as the length of that streak increased. The exception to this pattern was for sequences generated by a random mechanical device with a fixed base rate of.50. Under this specific condition, participants exhibited a bias toward reversal of streaks, and this bias was larger when participants were asked to make a dichotomous choice versus a numerical probability rating. We review alternate accounts for the anomalous judgments of sequences and conclude with our favored interpretation that is based on Rabin's version of Tversky & Kahneman's Law of Small Numbers. (shrink)

The business environment is increasingly uncertain due to the rapid development of disruptive information technologies, the changing global economy, and the COVID-19 pandemic. This brings great uncertainties to investors to predict the performance changes and risks of companies. This research proposes a sequential data-based framework that aggregates data from multiple sources including both structured and unstructured data to predict the performance changes. It leverages data generated from the early risk warning system in China stock market to measure and predict organization (...) performance changes based on the risk warning status changes of public companies. Different from the models in existing literature that focus on the prediction of risk warning of companies, our framework predicts a portfolio of organization performance changes, including business decline and recovery, thus helping investors to not only predict public company risks, but also discover investment opportunities. By incorporating sequential data, our framework achieves 92.3% macro-F1 value on real-world data from listed companies in China, outperforming other static models. (shrink)

Purpose: The main purpose of the study is to generate an idea about the validity of data-driven predictive analytics related to sustainable banking investment portfolio development. Methodology: Depending on the positivism philosophy, the researcher has focused on generating a hypothesis that can be evaluative and generate an outcome related to the topic under discussion. The descriptive design of the study has helped the researcher develop an idea about the different perspectives related to sustainable investment and ESG goals. The primary quantitative (...) data analysis is conducted by purposefully generating a sample of 101 participants, coming from the managerial level dignitaries from 10 leading banking sectors of the European continental financial market. Results: Analysing the opinions given by the respondents by IBM SPSS software, the results of the study. The results show that most of the male participants have shown their interest in participating in the survey which indicates the career growth of male bankers to be higher than the female ones. The most participating age group is found to be the people belonging to the age group of 21 to 35. The mean value of the correlation coefficient is found to be 0.921 and the mean value of the coefficient of determination is found to be 0.848. On the other hand, the regressive degree of freedom is found to be 11 which indicates the linear relationship present within different variables that are considered generally by the respondents. Conclusion: The interdependence of the variables clearly shows that the market trend regarding the future of the sustainable investment sector present within the banking field can be developed based on predictive analytics inclusion within the level of investment that is focused on meeting the ESG goals for improving sustainability. These factors act as the main motivators for the cross-departmental teams that act simultaneously for performing other tasks along with maintenance of predictive modelling of real-time data to evaluate the future of the banking investment sector. (shrink)

Behavioral design of so called »persuasive computer technologies« is the result of a merger between psychology, economics, and computer engineering. The article discusses its genealogy from the strategic response of military, governmental, and academic players to the general problem that the behavior of complex systems such as humans, societies, or markets is difficult to predict, and that controlling these complex systems means shaping them by designing their technological and social environments.

This paper examines the epistemological arguments about markets and planning that emerged in a series of unpublished exchanges between Hayek and Neurath. The exchanges reveal problems for standard accounts of both the socialist calculation debates and logical empiricism. They also raise questions concerning the sources of ignorance and uncertainty in modern economies, and the role of market and non-market organisations in the distribution and coordination of limited knowledge, which remain relevant to contemporary debates in economics. Hayek had argued that (...) Neurath's work exemplified the errors of rationalism that underpinned the socialist project. In response Neurath highlighted assumptions about the limits of reason and predictability that the two theorists shared and attempted to turn those assumptions back against Hayek in a defence of the possibility of socialist planning. The paper critically compares Neurath's and Hayek's criticisms of rationalism and considers how far Neurath is successful in his attempt to employ Hayek's assumptions against Hayek himself. (Published Online April 18 2006) Footnotes1 I would like to thank the staff at the Vienna Circle Institute for their assistance in consulting their copy of the Neurath Nachlass. I owe a debt of gratitude to Thomas Uebel and David Archard for their comments on earlier versions of this paper. I would like to acknowledge the support of the Arts and Humanities Research Board and a Manchester University Hallsworth Fellowship in writing this paper. (shrink)

An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what (...) is more properly called ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios. (shrink)

We argue that many modern marketing techniques are morally problematic because they take advantage of a phenomenon known as ‘ego-depletion’ according to which willpower is, similar to physical strength, a limited resource that can be depleted by predictable factors. We argue that this is impermissible for the same reason that spiking someone’s drink to impair their judgment is impermissible.

An important part of what it means for agents to be situated in the everyday world of human affairs includes their engagement with economic practices. In this paper, we employ the concept of cognitive institutions in order to provide an enactive and interactive interpretation of market and economic reasoning. We challenge traditional views that understand markets in terms of market structures or as processors of distributed information. The alternative conception builds upon the notion of the market as a “scaffolding (...) institution.” Introducing the concept of market as a “socially extended” cognitive institution we go beyond the notion of scaffolding to provide an enactive view of economic reasoning that understands the market participant in terms of social interactive processes and relational autonomy. Markets are more than inert devices for information processing; they can be viewed as “highly scaffolded,” where strong constraints and incentives predictably direct agents’ behavior. Building on this idea we argue that markets emerge from (a) the economic interaction of both supply and demand sides, in continual and mutual interplay, and (b) more basic social interactions. Consumer behavior in the marketplace is complex, not only contributing to determine the market price, but also extending the consumer’s cognitive processes to reliably attain a correct evaluation of the good. Moreover, this economic reasoning is socially situated and not something done in isolation from other consumers. From a socially situated, interactive point of view buying or not buying a good is something that enacts the market. This shifts the status of markets from external institutions that merely causally affect participants’ cognitive processes to social institutions that constitutively extend these cognitive processes. On this view the constraints imposed by social interactions, as well as the possibilities enabled by such interactions, are such that economic reasoning is never just an individual process carried out by an autonomous individual, classically understood. In this regard, understanding the concept of relational autonomy allows us to see how economic reasoning is always embodied, embedded in, and scaffolded by intersubjective interactions, and how such interactions make the market what it is. (shrink)

The Austrian School seems to remain outside the debate on the realism of economic models. In principle, given the association of the term “model” with the Chicago School, and also for understanding that Hayek had critized the model of perfect competition as unrealistic. Even though in previous opportunities we showed how the theory of market as a process could be understood as the model of the Austrian School, and that Hayek’s criticism to the model of perfect competition was not so (...) much of unrealistic, but that it was wrongly stated (Zanotti & Borella, 2015), and we developed the ontological foundation of the model in Hayek’s thought (Borella, 2019), in this paper we will show how it is posible to compare pattern predictions with models, and how in this sense, the Austrian School, also seems to get near, even without sufficiently noticing it, to the discussion on models in economics and their realism. Keywords: Realism of models- Hayek- pattern predictions-models . (shrink)