Hidden Preferences Research Articles

Information Transparency With Targeting Technology for Online Service Operations Platform

Social technologies have enabled the emergence of online platforms that provide offline service consultations and recommendations. In this environment, economic inefficiency arises when customers are not fully aware of their horizontally differentiated preferences. With its expertise or data dominance, a platform can be more informed about customers’ hidden preferences. We focus on an instrumental social technology, that is, targeting, which is a type of data-driven personalized information provision to manipulate customers’ beliefs about service quality. We propose a Hotelling model wherein customers are sensitive to the delays for service while making Bayesian belief updates based on a platform’s recommendations. When customers self-select their favorite service, their choices impose negative externalities through congestion and welfare loss. Our results indicate that service recommendations allow customers to navigate toward the more appropriate service, thus improving matching efficiency, reducing congestion costs, and enhancing aggregate customer welfare. We further identify the role of “information transparency” and study how the platform should strategically release information by making personalized service recommendations to customers. Interestingly, when a customer-centric platform maximizes aggregate customer welfare, we identify the “value of opaqueness” by strategically withholding service recommendations from a subset of customers and notice that this effect is more pronounced for a profit-seeking platform. Our results offer a better understanding of information transparency policies in the joint design of service recommendation systems and pricing mechanisms.

A fuzzy MCDM approach to support customer-centric innovation in virtual reality (VR) metaverse headset design

Nowadays, customer-centric innovation is important in affective design, leading to the design and development of a product that fits the needs of a group of target customers for sales and marketing. Though there is much research on customer-centric innovation and affective product design, designing a novel and innovative product that appeals to customers remains difficult. This is not only due to the difficulty of knowing a customer's preference, enabling the product functionality, etc., but it is also very complex to optimize both the technical and aesthetical design factors and parameters for a group of customers. The technical design specification is one of the critical aspects in designing innovative technological products. In fact, a similar group of target customers usually has hidden preferences that provide us with clues for identifying the design of a product. To this end, we propose a sophisticated optimization technique using the Hybrid Fuzzy-based Analytical Hierarchy Process and the Integral-based Taguchi Method called the Fuzzy multi-criteria decision-making (MCDM) approach to determine the essential factors and design parameters in order to understand the preferences of customers and the technical requirements of engineers for designing the enclosure of a product. We demonstrate our methodology using the virtual reality (VR) Headset, which can be used for visualizing the metaverse in the virtual environment. The fuzzy MCDM approach suggested combines technical and aesthetical features to enhance the robustness of product design.

Uncovering gender bias in attitudes towards financial advisors

This study examines the effect of gender of financial advisors on the perception of the quality of investment advice. Based on expectation states theory (EST) as well as Social Role Theory (SRT), we aim to understand whether the perception of the quality of financial advice is affected by gender bias; that is, do people trust the ability of male financial advisors to earn profits more than female financial advisors’ ability to do so? To analyze our assessments, we interviewed female financial advisors and conducted two quantitative studies. The first quantitative study, in which 1,600 respondents participated, examined whether the financial advisor's gender impacts how subjects appraise the probability of success versus the level of loss. This was followed by a second study designed to examine the presence of hidden gender bias by applying a conjoint analysis model. The results, as reflected from the interviews, indicated that female financial advisors do not feel discriminated against by clients on the basis of gender. This finding is consistent with the results of the first quantitative study, which showed gender bias was latent and revealed only in particular cases, such as in the case of a high investment amount. However, the conjoint analysis results, which explored more hidden preferences, indicated that the advisor's gender is one of the most important factors influencing a consumer's decision when choosing an investment advisor. Hence, female advisors’ gender was found to have a negative effect on the desire to invest, and this negative attitude was found to be significantly higher among male respondents. Thus, the first study's result showed that when asked directly, participants are indifferent as to the financial advisor's gender, however, ideally people prefer male advisor over female, as shown in the second study. A summary of the findings shows that a perceptual change is still needed in order to reduce negative perceptions towards female advisors.

Hidden preferences in social responsibility The analysis of erste seeds corporate social responsibility programme

Banks are usually active in creating and operating corporate social responsibility (CSR) programmes for different reasons. This paper analyses the Erste Bank SEEDS programme, which is dedicated to entrepreneurs promoting social welfare in Hungary. A total number of 68 social entrepreneurs were selected from 203 applicants to participate in the programme. To detect the success factors, the paper examines all proposals with content analysis. Then, using a bivariate logistic regression, the research estimates the probability of getting selected and identifies three critical factors: the potential social impact, financial sustainability and the life cycle of the product or service. These results hint to the bank caring about social impacts but only if financial sustainability is assured. Some social activities (community building, health or sustainable lifestyle) are less preferred than others (employment). The main contribution of the study is to present how corporate social responsibility programmes can be analysed and what hidden requirements may be incorporated within them.

Revealed preference in argumentation: Algorithms and applications

Argumentative agents in AI are inspired by how humans reason by exchange of arguments. Given the same set of arguments possibly attacking one another (Dung's AA framework) these agents are bound to accept the same subset of those arguments (aka extension) unless they reason by different argumentation semantics. However humans may not be so predictable, and in this paper we assume that this is because any real agent's reasoning is inevitably influenced by her own preferences over the arguments. Though such preferences are usually unobservable, their effects on the agent's reasoning cannot be washed out. Hence by reconstructing her reasoning process, we might uncover her hidden preferences, which then allow us to predict what else the agent must accept. Concretely we formalize and develop algorithms for such problems as uncovering the hidden argument preference relation of an agent from her expressed opinion, by which we mean a subset of arguments or attacks she accepted from a given AA framework; and uncovering the collective preferences of a group from a dataset of individual opinions. A major challenge we addressed in this endeavor is to deal with “answer sets” of argument preference relations which are generally exponential or even infinite. So we start by developing a compact representation for such answer sets called preference states. Preference revelation tasks are then structured as derivations of preference states from data, and reasoning prediction tasks are reduced to manipulations of derived preference states without enumerating the underlying (possibly infinite) answer sets. We also apply the presented results to two non-trivial problems: learning preferences over rules in structured argumentation with priorities – an open problem so far; and analyzing public polls in apparently deeper ways than existing social argumentation frameworks allow.

Understanding the Role of Incentive Salience in Sexual Decision-Making.

In the search for understanding female sexual decision-making, progress has been made in uncovering a variety of perceptual biases and most of these concern the animal's sensory biology and cognitive processes. We are now poised to dig deeper into the female's decision-making and ask if incentive salience, which plays a role in all types of appetitive behaviors, also influences a female's "taste for the beautiful." The incentive salience hypothesis suggests that dopamine assigns value or salience to objects or actions. After value is assigned to all potential actions, an action selection system then chooses among potential options to select the most valuable action. In this view, dopamine stimulates reward-seeking behavior by assigning incentive salience to specific behavioral actions, which in turn, increases pursuit and focus on objects or stimuli that represent the valuable action. Here, we apply this framework to understand why females are compelled to respond maximally to some male courtship signals over others and how this process may reveal a female's hidden mate preferences. We examine studies of dopamine and the mesolimbic reward system because these may play a role in expanding the female's perceptual landscape for novelty in male courtship signals and establishing novel hidden preferences. We review three avenues of research that may identify signatures of incentive salience in females during sexual decision-making. This review includes studies of dopamine agonist or antagonist administration in females during mate choice or partner preference tests, measures of neural activity in dopaminergic neural circuits during mate choice or partner preference tests, and social regulation of dopamine in females when entering reproductive contexts and/or exposure to mate signals. By applying the incentive salience hypothesis to female reproductive decision-making, it redefines how we see the female's role in sexual encounters. Females cannot be considered passive during reproductive encounters; rather they are seeking sexual encounters, particularly with males that tap into their perceptual biases and initiate a reward-seeking response. Incentive salience applied to reproductive behavior requires considering females as viewing sexual stimuli as rewarding and initiating action to seek out this reward, all of which indicates females are driving sexual encounters.

Electoral Accountability and Responsive Democracy

AbstractWe consider elections with hidden preferences and hidden actions, in which neither voters nor politicians can commit to future choices. When politicians are highly office motivated, they respond by choosing high policies to signal that they are above average, and some below-average politicians must randomise between choosing policies near their ideal points and mimicking above-average politicians by choosing high policies. If voter preferences are increasing, then elections deliver positive outcomes; but if voter preferences are single peaked, then politicians overshoot in the first period. Electoral incentives shift to sanctioning, rather than selection, as office motivation becomes large.

873-P: "I Had Nobody to Represent Me"—How Patient Perceptions of Diabetes Health Care Providers' Age, Gender, and Ethnicity Impact Shared Decision-Making

Background: When patients and clinicians establish diabetes care plans together, patients are more likely to successfully manage their diabetes. Although studies reveal diabetes healthcare provider (HCP) bias and healthcare inequities, the potential impact of patient perceptions of their diabetes HCP is unknown. We explored how patient perceptions of their diabetes HCP influences treatment plans and shared decision-making (SDM). Methods: Adult participants with diabetes receiving diabetes care at community sites, primary care, or specialty clinics were invited to participate in semi-structured 30- to 45-minute interviews. Interviews were conducted from Nov 2018 to Jan 2019. Responses were qualitatively analyzed with NVivo for emergent themes. Results: We conducted 28 interviews with participants aged 34-81. Participants were female (16), male (11), FtM (1), and white (9), black/Caribbean (11), Asian/South Asian (4), Middle Eastern (2) and Hispanic (2). We identified the following themes: 1) Hidden bias for stated preference of HCP ethnicity: While most participants stated no preference, we identified hidden preferences via cultural relatability as well as evidence of color blindness. 2) Impact of HCP characteristics on SDM: Participants felt junior HCPs had up-to-date knowledge, while others preferred senior HCPs’ clinical experience. HCP gender and ethnicity impacted SDM via relatability; participants who related to their HCP were more likely to engage in a care plan. 3) Other factors affecting HCP preference: Participants preferred HCPs who provided patient-centered care, characterized by individualized referrals, trusting relationships, and HCPs’ investment in patient success. Conclusion: Diabetes affects persons of all ages, genders, and ethnicities. Exploring how patient perceptions of HCPs inform shared decision-making offers insight into systemic barriers to equitable person-centered care. Disclosure T. Ahmad: None. S. Hari: None. D.E. Cleary: None. C. Yu: None.

SPICE Frame Work: A Tool for Analysis of Large Processual Data

The paradigm wars have led to dominance of instrumentality over purpose in the application of research methods resulting in journals showing distinct but hidden preferences for methodology driven articles. The bigger impact has been very poor theory development. Theories are crucially dependent upon establishment of causal linkages between factors identified in a phenomenon. It requires qualitative case based methodology to generate it and once established quantitative methods allow extension and validation of the theory. The very process of theory generation has been hampered by the debate on issues of validity and replication. What limits in numbers of cases must be sourced to successfully establish causal phenomenon is still a subject of debate. When multiple cases are used as part of case research strategy, issues of presentation of findings and critically ability to use strategies in theorirising from volume of process data come to the fore. Missing is a framework which would enable use of multiple case based designs regularly in research on management and allow use of generic heuristics to analyze process data. This paper generates such a generic framework. “SPICE” – an acronym standing for five generic dimensions-enables processual analysis of data from multiple case research designs. It generates patterns from the analysis resulting in establishing of causal mechanisms leading to generation of hypotheses and their verification. This framework will enable greater use of multiple case based research designs and thus better ways to generate theories.

The Economic Value of Book Loans – The Case of the Municipal Library of Prague

AbstractIn recent years, the value of public library services has tended to be evaluated using the contingent valuation method. This is able to reveal readers’ hidden preferences. However, it appears that the total value of a public library as expressed by one number leads to a methodological bias related to the difficulty of assessing the value of one standard book loan. Moreover, this approach does not reflect the needs of public libraries’ management. Therefore, this study focuses on a detailed investigation of the most important public library service, book loans. For this purpose, we have employed statistical models to identify the significant determinants of willingness to pay (WTP) for book loans and WTP for purchasing books, respectively. The results suggest that loan services have the greatest perceived value for students, people who are economically active. Management who are interested in increasing the success of their institution across society must also know the perceived value of individual books so that they can adjust their library's acquisition policy. It was found that novels had the highest perceived value, followed by educational literature in the fields of economics and education as well as information science. This entirely new analytical method of measuring WTP for public services raises many new implications that could change the strategies of individual libraries – as well as the public policy strategies of donors and library ownership.

Electoral Accountability and Responsive Democracy

We consider a canonical two-period model of elections with adverse selection (hidden preferences) and moral hazard (hidden actions), in which neither voters nor politicians can commit to future choices. We prove existence of electoral equilibria, and we show that office holders mix between “taking it easy” and “going for broke” in the first period. Even in the presence of a finite horizon, we establish that increasing office motivation leads to arbitrarily high expected policy outcomes. We conclude that the mechanism of electoral accountability has the potential to achieve responsiveness of democratic politics when electoral incentives are sufficiently large.

Sensory ecology and perceptual allocation: new prospects for neural networks

Sensory ecology provides a conceptual framework for considering how animals ought to design sensory systems to capture meaningful information from their environments. The framework has been particularly successful at describing how one should allocate sensory receptors to maximize performance on a given task. Neural networks, in contrast, have made unique contributions to understanding how 'hidden preferences' can emerge as a by-product of sensory design. The two frameworks comprise complementary techniques for understanding the design and the evolution of sensation. This article reviews empirical literature from multiple modalities and levels of sensory processing, considering vision, audition and touch from the viewpoints of sensory ecology and neuroethology. In the process, it presents modifications of extant neural network algorithms that would allow a more effective integration of these diverse approaches. Together, the reviewed literature suggests important advances that can be made by explicitly formulating neural network models in terms of sensory ecology, by incorporating neural costs into models of perceptual evolution and by exploring how such demands interact with historical forces.

Nonparallel coevolution of sender and receiver in the acoustic communication system of treefrogs

Advertisement calls of closely related species often differ in quantitative features such as the repetition rate of signal units. These differences are important in species recognition. Current models of signal/receiver co-evolution predict two possible patterns in the evolution of the mechanism used by receivers to recognize the call. (1) Classical sexual selection models (Fisher-Process, good-genes/indirect benefits, direct benefits models) predict that close relatives use qualitatively similar signal recognition mechanisms tuned to different values of a call parameter. (2) Receiver bias models (hidden preference, pre-existing bias models) predict that if different signal recognition mechanisms are used by sibling species, evidence of an ancestral mechanism will persist in the derived species, and evidence of a pre-existing bias will be detectable in the ancestral species. We describe qualitatively different call recognition mechanisms in sibling species of treefrogs. Whereas Hyla chrysoscelis uses pulse rate to recognize male calls, H. versicolor uses absolute measurements of pulse duration and interval duration. We found no evidence of either hidden preferences or pre-existing biases. The results are compared with similar data from katydids (Tettigonia sp.). The data are discussed with regard to current models of signal/receiver co-evolution.

The role of ‘hidden preferences’ in the artificial co-evolution of symmetrical signals

Recently, within the biology literature, there has been some interest in exploring the evolutionary function of animal displays through computer simulations of evolutionary processes. Here we provide a critique of an exploration of the evolutionary function of complex symmetrical displays. We investigate the hypothesis that complex symmetrical signal form is the product of a ‘hidden preference’ inherent in all sensory systems (i.e. a universal sensory bias). Through extending previous work and relaxing its assumptions we reveal that the posited ‘hidden preference’ for complex symmetry is in reality a preference for homogeneity. The resulting implications for further accounts of the evolutionary function of complex symmetrical patterning are considered.

An exaggerated preference for simple neural network models of signal evolution?

Recently, simple neural network models have been used to explain the evolution of important phenomena in animal signalling, such as extravagant ornamentation and symmetrical signals, as responses to inevitable 'hidden preferences' of recognition systems. We argue that these very simple models may be misleading because they may not behave in important ways like the recognition systems of real animals and so cannot justify their claim to demonstrate general principles of perception in a signalling context. We show that the way in which these simple models respond to exaggerated signals may not be, as is claimed, a close parallel to the phenomena of peak shift or supernormal responses. We also argue that the preference for symmetrical patterns shown by the models is unlikely to reflect the way computationally that real animals solve problems of pattern invariance and may be an artefact of the particular way the models have been set up. Whereas more sophisticated neural net models do capture known properties of real visual systems and are consequently of great use in understanding perception, the same cannot be said of very simple one-dimensional models with small numbers of units and connections. Given the far reaching explanatory claims made of these simpler models their limitations should be more widely recognized.

Hidden preferences and the evolution of signals

Mechanisms concerned with the recognition of biological signals act as important agents of selection on the appearance or ‘form’ of signals. Recognition of a given signal form can be achieved by many equally efficient alternative mechanisms. These alternative mechanisms will be selectively neutral and subject to change by genetic drift, thus preventing the fixation of a signal form that is optimal in releasing the receiver’s behaviour. Because signal form is a multidimensional trait with an almost infinite potential to vary, it is expected that some novel forms of signals always exist that elicit responses more readily than any of those signals that the receiver has experienced during its evolutionary history. The existence of such ‘hidden preferences’ is illustrated in simple models of recognition mechanisms based on artificial neural networks. The fundamental evolutionary instability of recognition mechanisms perhaps explains why biological signals are so variable in form, and why, in experiments, animals sometimes show greater responses to novel forms of signals than they do to familiar forms.