Augmented Human Machine Intelligence for Distributed Inference

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and solve problems. Efficient fusion of information from both human and sensor sources is expected to improve system performance and enhance situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves. In this dissertation, we consider that people often have a number of biases and rely on heuristics when exposed to different kinds of uncertainties, e.g., limited information versus unreliable information. We develop novel theoretical frameworks for collaborative decision making in complex environments when the observers may include both humans and physics-based sensors. We address fundamental concerns such as uncertainties, cognitive biases in human decision making and derive human decision rules in binary decision making. We model the decision-making by generic humans working in complex networked environments that feature uncertainties, and develop new approaches and frameworks facilitating collaborative human decision making and cognitive multi-modal fusion. The first part of this dissertation exploits the behavioral economics concept Prospect Theory to study the behavior of human binary decision making under cognitive biases. Several decision making systems involving humans\u27 participation are discussed, and we show the impact of human cognitive biases on the decision making performance. We analyze how heterogeneity could affect the performance of collaborative human decision making in the presence of complex correlation relationships among the behavior of humans and design the human selection strategy at the population level. Next, we employ Prospect Theory to model the rationality of humans and accurately characterize their behaviors in answering binary questions. We design a weighted majority voting rule to solve classification problems via crowdsourcing while considering that the crowd may include some spammers. We also propose a novel sequential task ordering algorithm to improve system performance for classification in crowdsourcing composed of unreliable human workers. In the second part of the dissertation, we study the behavior of cognitive memory limited humans in binary decision making and develop efficient approaches to help memory constrained humans make better decisions. We show that the order in which information is presented to the humans impacts their decision making performance. Next, we consider the selfish behavior of humans and construct a unified incentive mechanism for IoT based inference systems while addressing the selfish concerns of the participants. We derive the optimal amount of energy that a selfish sensor involved in the signal detection task must spend in order to maximize a certain utility function, in the presence of buyers who value the result of signal detection carried out by the sensor. Finally, we design a human-machine collaboration framework that blends both machine observations and human expertise to solve binary hypothesis testing problems semi-autonomously. In networks featuring human-machine teaming/collaboration, it is critical to coordinate and synthesize the operations of the humans and machines (e.g., robots and physical sensors). Machine measurements affect human behaviors, actions, and decisions. Human behavior defines the optimal decision-making algorithm for human-machine networks. In today\u27s era of artificial intelligence, we not only aim to exploit augmented human-machine intelligence to ensure accurate decision making; but also expand intelligent systems so as to assist and improve such intelligence

Cryptocurrency Rewards and Crowdsourcing Task Success

Crowdsourcing task success depends on the contributions of developers. How to identify capable developers and motivate them to actively contribute to a task is a challenging issue. This study investigates how the use of cryptocurrency rewards, i.e., the choices of stablecoins and unstablecoins affects the crowdsourcing task success, and how the relationship depends on task difficulty. Based on 3858 crowdsourcing tasks, we find that the use of unstablecoins reduces the number of participating contributors and extends the time period of having the first contributor, but has no significant effect on the likelihood of task success. In addition, task difficulty alleviates the negative effect of the unstablecoins on the number of participants. Our study potentially provides important implications for the use of cryptocurrency tokens as task rewards

Public Leaderboard Feedback in Sampling Competition: An Experimental Investigation

We investigate the role of performance feedback, in the form of a public leaderboard, in a sequential-sampling contest with costly observations. The player whose sequential random sample contains the observation with the highest value wins the contest and obtains a prize with a fixed value. We find that there exist parameter configurations such that in the subgame perfect equilibrium of contests with a fixed ending date (i.e., finite horizon), providing public performance feedback results in fewer expected observations and a lower expected value of the winning observation. We conduct a controlled laboratory experiment to test the theoretical predictions, and find that the experimental results largely support the theory. In addition, we investigate how individual characteristics affect competitive sequential-sampling activity. We find that risk aversion is a significant predictor of behavior both with and without leaderboard feedback, and that the direction of this effect is consistent with the theoretical predictions

Improved mutual fund investment choice architecture

Two choice architecture interventions were explored to debias investors’ irrational preference for mutual funds with high past returns rather than funds with low fees. A simple choice task was used involving a direct trade-off between maximizing past returns and minimizing fees. In the first intervention, warning investors that, “Some people invest based on past performance, but funds with low fees have the highest future results” was more effective than three other disclosure statements, including the US financial regulator’s, “Past performance does not guarantee future results”. The second intervention involved converting mutual fund annual percentage fees into a 10 year dollar cost equivalent. This intervention also improved investors’ fee sensitivity, and remained effective even as past returns increased. Financially literate participants were surprisingly more likely to irrationally maximize past returns in their investment choices

Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023

Determinations of Other-Regarding Behavior and the Private Provision of Public Goods

Diese Arbeit leistet einen Beitrag zur Forschung, die die im ökonomischen Standardmodell verwendete Annahme des eng gefassten Eigeninteresses in Frage stellt. Der erste Teil der Arbeit umfasst drei experimentelle Studien über soziale Präferenzen. Der zweite Teil der Arbeit umfasst drei Studien, die sich in die Literatur zur privaten Bereitstellung öffentlicher Güter einfügen. Jahrzehntelange experimentelle Forschung mit öffentlichen Gütern, Ultimatum-, Vertrauens- und Diktatorspielen hat gezeigt, dass Individuen selbst bei anonymen One-Shot-Entscheidungen auf monetäre Auszahlungen zum Nutzen anderer verzichten. Eine Erklärung für dieses Verhalten ist, dass Individuen eine Präferenz für eine gerechte Verteilung haben. Das Verhalten hängt jedoch nicht nur von der Verteilung der Auszahlungen ab, sondern auch von dem Prozess, der zu dieser Verteilung führt. Zu den prozessbezogenen Merkmalen gehören die Absicht der beteiligten Entscheidungsträger und damit zusammenhängend das Ausmaß, in dem ihnen kausal die Verantwortung zugeschrieben werden kann, die Fairness der Verfahren oder die Einhaltung bestimmter sozialer oder moralischer Normen. Alle drei Studien im ersten Teil dieser Arbeit untersuchen das Verhalten in Situationen, in denen Entscheidungen wahrscheinlich von den Folgen für die Verteilung der Auszahlungen beeinflusst wird, in denen aber wahrscheinlich auch andere Determinanten eine Rolle spielen. Die Studien untersuchen (1) die Rolle einer fairen Verteilung auf das Verbraucherverhalten bei Preisdiskriminierung, (2) wie verschiedene Ausreden, die sich aus der Ungewissheit über die Folgen des eigenen Handelns für andere ergeben, egoistisches Verhalten beeinflussen und (3) die Rolle der Beobachtbarkeit von Unehrlichkeit beim Lügen in einer Online-Umgebung. Das Versagen der Märkte, ein effizientes Maß an öffentlichen Gütern bereitzustellen, wird als einer der Hauptgründe für staatliche Eingriffe angesehen. Die jahrzehntelange Forschung in der Verhaltensökonomie zeigt jedoch, dass die privaten Beiträge im Allgemeinen höher sind, als es das reine Eigeninteresse vorhersagt. Wissenschaftler sind seit langem daran interessiert, die zugrunde liegenden Präferenzen für private Beiträge zu öffentlichen Gütern zu verstehen. Diese Forschung hat eine Reihe von Motiven für Beiträge untersucht, die über das reine Eigeninteresse hinausgehen, hat aber auch zu möglichen Erklärungen geführt, die in den Bereich des Eigeninteresses fallen, wie etwa ein kurzfristiger positiver Grenznutzen oder langfristige Signalanreize. Der zweite Teil dieser Arbeit kann in diese Literatur eingeordnet werden. Studie vier untersucht die Auswirkung von Gamification auf die intrinsische Motivation und die Leistungserbringung bei einer Aufgabe, die wichtige Merkmale von sogenannten Micro-tasks nachahmt, wie sie typischerweise in Crowdsourcing-Projekten für öffentliche Güter vorkommen. Die Studien fünf und sechs untersuchen digitale öffentliche Güter im Kontext der EU-Chemikalienverordnung REACH, bewerten, wie effektiv das öffentliche Gut bereitgestellt wird, und diskutieren mögliche Anpassungen, um die Anreize für Personen zu erhöhen, die freiwillig zu dem Gut beitragen.This thesis contributes to research that challenges the narrow self-interest assumption used in the standard economic model. The first part of the thesis includes three experimental studies on social preferences. The second part of the thesis includes three studies that fit into the literature on private provision of public goods. Decades of experimental research with public goods, ultimatum, trust, and dictator games have shown that individuals forgo monetary payoffs for the benefit of others even in anonymous one-shot decisions. One explanation for this behavior is that individuals have a preference for fair distribution. However, behavior depends not only on the distribution of payoffs, but also on the process that leads to that distribution. Process-related characteristics include the intent of the decision makers involved and, relatedly, the extent to which causal responsibility can be attributed to them, the fairness of the procedures, or the adherence to certain social or moral norms. All three studies in the first part of this paper examine behavior in situations where decisions are likely to be influenced by consequences for the distribution of payoffs, but where other determinants are also likely to play a role. The studies examine (1) the role of a fair distribution on consumer behavior in the presence of price discrimination, (2) how various excuses arising from uncertainty about the consequences of one's actions for others influence selfish behavior, and (3) the role of observability of dishonesty in lying in an online environment. The failure of markets to provide an efficient level of public goods is seen as one of the main reasons for government intervention. However, decades of research in behavioral economics show that private contributions are generally higher than pure self-interest predicts. Scholars have long been interested in understanding the underlying preferences for private contributions to public goods. This research has explored a range of motivations for contributions that go beyond pure self-interest, but has also led to possible explanations that fall within the realm of self-interest, such as short-term positive marginal utility or long-term signaling incentives. The second part of this thesis can be placed in this literature. Study four examines the impact of gamification on intrinsic motivation and performance in a task that mimics important features of so-called micro-tasks typically found in public goods crowdsourcing projects. Studies five and six examine digital public goods in the context of the EU REACH chemicals regulation, assess how effectively the public good is delivered, and discuss possible adaptations to increase incentives for individuals who voluntarily contribute to the good.2021-10-1

Male Weight Control: Crowdsourcing and an Intervention to Discover More

Men and women have similar rates of obesity but the combined prevalence of overweight and obesity is higher among men. Men who are overweight are a high-risk group for many obesity-related chronic diseases, as they are more likely to carry excess weight in the abdomen, which is generally more harmful than weight stored in the lower body. Men are also less likely than women to perceive themselves as overweight, and thus are less likely to initiate weight loss through organized weight loss programs. On average, less than 27% of weight loss trial participants have been men. Internet-based research is a low-cost, efficient way to produce novel hypotheses related to weight loss that may have previously escaped weight loss professionals. Additionally, incentives are an effective tool to motivate behavior change, and there is ample evidence to support the use of incentives to encourage many health-promoting behaviors, such as weight loss. The purpose our initial study was to facilitate intervention development by using crowdsourcing to detect unexpected beliefs and unpredicted barriers to male weight loss. The aim of our main study was to evaluate the impact of financial incentives to facilitate weight loss in men, delivered as part of a weight loss intervention. Two separate studies were conducted. In the first project, participants were recruited to a crowdsourcing survey website which was used to generate hypotheses for behaviors related to overweight and obesity in men. Participants provided 21,846 responses to 193 questions. While several common themes seen in prior research were revealed such as previous health diagnoses and physical activity participation, other potential weight determinants such as dietary habits, sexual behaviors and self-perception were reported. Crowdsourcing in this context provides a mechanism to further investigate perceptions of weight and weight loss interventions in the male population that have not previously been documented. These insights will help guide future intervention design. For the main project, a randomized trial compared the Gutbusters weight loss program (based on the REFIT program) alone with Gutbusters with escalating incentives for successful weight loss. The six-month intervention was conducted online with weekly in-person weight collections for the first 12 weeks. Gutbusters encouraged participants to make six 100-calorie changes to their daily diet, utilizing a variety of online lessons targeting specific eating behaviors. Measures included demographic information, height, weight, waist circumference, and body fat percentage. Participants (N=102, 47. 0± 12. 3 yrs old, 32. 5 kg/m2, 80. 4% with at least two years of college) were randomized in a 1:1 ratio to Gutbusters or Gutbusters+Incentive. Significantly more Gutbusters+Incentive participants lost at least 5% of their baseline weight compared to the Gutbusters group at both 12 and 24 weeks. Similar to the aforementioned REFIT program, Gutbusters participants were able to achieve clinically significant weight loss. The Gutbusters+Incentive achieved greater rates of weight loss than the Gutbusters alone group, further supporting the value of incentives in promoting health behaviors

The adoption of algorithmic decision-making agents over time: algorithm aversion as a temporary effect?

Many individuals encounter algorithmic decision-making agents with algorithm aversion – the irrational discounting of superior algorithmic advice. So far, we know little about how algorithm adoption develops over time and how people may overcome algorithm aversion. In response, we explore the factors that foster the adoption of algorithmic decision-making agents – initially and over time. Based on an experiment with incentive-compatible awards over several rounds, we find that one’s knowledge about peers successfully using the technology as well as low transaction costs serve as strong initial motivators to foster initial algorithm adoption. Further, by revealing that adoption rates increase and initial difference in adoption rates become smaller over time, we find evidence that despite the technology’s particularities, algorithm aversion seems to have a temporary effect only

An agent-based approach to customer crowd-shipping

Thesis (MEng)--Stellenbosch University, 2022.ENGLISH SUMMARY: The challenge of effective last-mile deliveries is progressively becoming more important with the acceleration in the e-commerce industry that is accompanied by a growing number of doorstep deliveries. Crowd logistics provides innovative solutions whereby ordinary people become in- volved in the execution of logistics operations. A particular crowd logistics initiative, referred to as customer crowd-shipping, recently gained interest from researchers after initial implemen- tations thereof had been performed by companies such as Walmart and Amazon. The approach involves the use of a retailer’s in-store customers, in addition to regular delivery vehicles, for delivering orders to online customers. Such in-store customers, referred to as occasional drivers, are offered incentives to deliver orders on their way home after visiting the retailer. In this thesis, an agent-based simulation model is proposed for studying the highly dynamic working of the customer crowd-shipping initiative. The model encompasses a traditional last- mile delivery system, complemented by the ability to utilise autonomous occasional drivers. The modelled traditional last-mile delivery system consists of a dedicated fleet of delivery vehicles serving online customers from a single depot. The execution of deliveries is formulated as a vehicle routing problem and subsequently solved by means of well-known vehicle routing heuristics. In addition, the occasional drivers are modelled as autonomous agents who have the ability to act outside of the control of the retailer. Rather than being assigned to particular orders, occasional drivers are presented with potential orders from which they may select an order suitable for them to deliver. Their decision to participate is modelled based on self- interest, where an occasional driver agent aims to maximise the difference between the incentive offered and his or her perceived value of the additional time required to deliver the order. An integrated approach to customer crowd-shipping is developed in order to consider the benefits for both the retailer and occasional drivers. This includes an incentive scheme and a method for identifying online customers as candidates for crowd-shipping. The latter involves the dynamic calculation of the company’s cost to serve an individual customer, which is determined for all online customers. Finally, user-friendly access to the agent-based simulation model is facilitated by a graphical user interface. The proposed model is subjected to systematic verification, ensuring the correct functioning and integration of its subcomponents. Moreover, the model is evaluated under various operating conditions to gain a deeper understanding of the crowd-shipping initiative, while simultaneously validating the model as adequate. In particular, parameter variation, sensitivity analyses, and scenario analyses are conducted, followed by face validation by subject matter experts. The results of the various analyses indicate that customer crowd-shipping may successfully function as an extension to an existing last-mile delivery system, with the potential of reducing both the total delivery cost and customer waiting time. These benefits are, however, shown to be influenced by the incentive scheme and the strategy by which online customers are se- lected as crowd-shipping candidates. Finally, it is deduced that the maturity of the customer crowd-shipping system and the occasional population’s perceived value of time influence the performance of the customer crowd-shipping model.AFRIKAANS OPSOMMING: Die uitdaging van doeltreffende laaste-myl aflewerings word geleidelik belangriker met die versnelling in die e-handelsbedryf wat gepaard gaan met ’n groeiende aantal voorstoepaflewerings. Skare-logistiek bied innoverende oplossings waardeur gewone mense betrokke raak by die uitvoering van logistieke bedrywighede. ’n Sekere skare-logistieke inisiatief, waarna verwys word as kli¨ente-skareversending, het onlangs belangstelling by navorsers ontlok nadat aanvanklike implementering daarvan deur maatskappye soos Walmart en Amazon plaasgevind het. Die benadering behels die gebruik van ’n kleinhandelaar se in-winkel kli¨ente, benewens normale afleweringsvoertuie, om bestellings by aanlynkli¨ente af te lewer. Sulke in-winkel kli¨ente, na wie daar ook verwys word as geleentheidsbestuurders, word aansporings gebied om bestellings op pad huis toe af te lewer nadat hulle die kleinhandelaar besoek het. In hierdie tesis word ’n agent-gebaseerde simulasiemodel voorgestel vir die bestudering van die hoogs-dinamiese werking van die kli¨ente-skareversendingsinisiatief. Die model sluit ’n tradisionele laaste-myl afleweringstelsel in, aangevul deur die mootlikheid om outonome geleentheidsbestuurders te gebruik. Die gemodelleerde tradisionele laaste-myl afleweringstelsel bestaan uit ’n toegewyde vloot afleweringsvoertuie wat aanlynkli¨ente vanaf ’n enkele depot bedien. Die uitvoering van aflewerings word as ’n voertuig-roeteringsprobleem geformuleer en vervolgens deur middel van bekende voertuig-roeteringsheuristieke opgelos. Daarbenewens word die geleentheidsbestuurders as outonome agente gemodelleer wat oor die vermo¨e beskik om buite die beheer van die kleinhandelaar op te tree. Eerder as om aan spesifieke bestellings toegewys te word, word geleentheidsbestuurders potensi¨ele bestellings aangebied waaruit hulle een kan kies wat geskik is om deur hulle afgelewer te word. Hul besluit om deel te neem berus op eiebelang, waar ’n geleentheidsbestuurder-agent poog om die verskil tussen die aansporing wat aangebied word en sy of haar waargenome waarde van die bykomende tyd wat benodig word om die bestelling af te lewer, te maksimeer. ’n Ge¨ıntegreerde benadering tot kli¨ente-skareversending word ontwikkel om die voordele vir beide die kleinhandelaar en geleentheidsbestuurders te oorweeg. Dit sluit ’n aansporingskema in sowel as ’n metode om aanlynkli¨ente as kandidate vir skareversending te identifiseer. Laasgenoemde behels die dinamiese berekening van die maatskappy se koste om ’n individuele kli¨ent te bedien, wat vir alle aanlynkli¨ente bepaal word. Laastens word gebruikersvriendelike toegang tot die agent-gebaseerde simulasiemodel deur ’n grafiese gebruikerskoppelvlak moontlik gemaak. Die voorgestelde model word aan sistematiese verifikasie onderwerp, wat die korrekte funksionering en integrasie van die deelkomponente daarvan verseker. Boonop word die model onder verskeie bedryfstoestande ge¨evalueer om ’n dieper begrip van die kli¨ente-skareversendingsinisiatief te verkry, terwyl die model terselfdertyd as voldoende bekragtig word. In die besonder word parametervariasie, sensitiwiteitsanalises en scenario-ontledings uitgevoer, gevolg deur sigvalidering deur vakkundiges. Die resultate van die verskillende ontledings dui daarop dat kli¨ente-skareversending suksesvol as ’n uitbreiding van ’n bestaande laaste-myl afleweringstelsel kan funksioneer, met die potensiaal om beide die totale afleweringskoste en kli¨entewagtyd te verminder. Daar word egter getoon dat hierdie voordele be¨ınvloed word deur die aansporingskema en die strategie waardeur aanlynkli ¨ente as skareversendingkandidate gekies word. Laastens word afgelei dat die volwassenheid van die kli¨ent-skareversendingstelsel en die bevolking geleentheidsbestuurders se waargenome waarde van tyd die prestasie van die kli¨ente-skareversendingsmodel be¨ınvloed.Master