Over the past decades, decision making under risk has garnered a great amount of attention both in the field of economics and psychology. Although state-dependent variabilities of risk taking are well-documented, little is known about the effects of a person’s preferred time of day, or chronotype, in risky decision making. Under circumstances of circadian mismatch (e.g., when an “early bird” makes decisions in the evening), research suggests that decision making may reflect a greater reliance on heuristics, such as using stereotypes in social judgments. However, the effects of circadian mismatch on heuristics in risky decision making are relatively unexplored. This paper looks into the effects of circadian mismatch on the reflection effect: a behavioral bias in financial decision making, wherein individuals are risk averse when facing potential gains, and risk seeking when facing potential losses. Participants will be randomly assigned to their circadian matched or circadian mismatched conditions and will play a series of financial gambling tasks with real monetary incentives. This study predicts that the reflection effect will be exacerbated in circadian mismatched individuals as compared to matched participants. Exploring such an effect could have real-world implications on decision making under risk by providing critical knowledge about the effects of time of day on our susceptibility to behavioral biases. It could therefore point to the existence of a more optimal time of day to engage in such critical decision making.

I avhandlingen beskrivs ett hypotetiskt system som kan användas av mobila användare, bland andra taxichaufförer, som exempelvis vill följa en viktig fotbollsmatch. Flera faktorer ställer till problem: Ibland står bilen still och föraren har inget annat att tänka på än matchen. Ibland kör denne runt med en kund som inte vill bli störd av matchen. Dessutom kan det vara svårt att titta på rörliga bilder och köra bil samtidigt. I och med att bilen körs runt har man också olika bra anslutning till Internet vid olika tillfällen – det kan variera mellan inget alls, en dålig GSM/GPRS förbindelse (8 kbps) och en snabb WLAN anslutning (100 Mbps). I avhandlingen presenteras en tre-lagers modell som kan användas för att beskriva den här typen av applikationers kvalitet. Modellen delas in i tre lager: nätverk, applikation och användare/använding. Det sistnämnda lagret ligger utanför det tekniska systemet och definieras av att det är där de verkliga informationsutbytet sker. På applikationsnivån samlas data in, packas och packas upp i samband med nätverkstransport och visas sedan för användaren. Det är också här som eventuell interaktion sker med användaren. Nätverkslagret är ansvarigt för ändamålsenlig transport av data. De tre lagren är ömsesidigt beroende av varandra. Dålig prestanda på ettlager påverkar de andra lagren och tvärtom. Tre studier har genomförts av hur problem på nätverkslagret i form av begränsad bandbredd och hög fördöjning påverkar användarna. Låg bandbredd ger låg videokvalitet vilket inte uppskattas av användarnamnen genom att skifta till animeringar som fungerar med lägre bandbredd kan man ändå få användarna nöjda. Om användarna måste välja mellandålig videokvalitet och animeringar väljer de som ser sig som fotbollskunniga det förstnämnda och de som ser sig som okunniga men dock fotbollsfans väljer det sistnämnda. Men i en annan studie där användarna spelade bluffstopp mot varandra över ett datanätverk fick vi ett annat resultat. Där var det negativt med högre videokvalitet (bilder per sekund). En förklaring kan vara att användarna distraherades mer av högre bildfrekvens. I den tredje studien studerades vad som händer i Pong om man läggerin fördröjningar i spelet. Sedan tidigare visste man att det blir svårare attspela med fördröjningar – särskilt om man inte märker dem. Vi ställde ossfrågan om man kan kompensera för dem genom att informera användarna om dem. Det visade sig att användare som får information med i vårtfall en prediktiv visning lättare anpassar sin mentala insats till uppgiftens svårighetsgrad. Det är alltså inte bara möjligt utan ibland också önskvärt att utnyttja en lägre bandbredd från användarens perspektiv. Med det sagt finns det ändå i långt fler situationer där det är bättre med bättre nätverksprestanda. Pongspelet var roligare med lägre delay. Videon uppfattades som bättre medhögre bandbredd i den förstnämnda studien. Multicast, där ett paket skickas till flera användare i stället för att de skafå varsin, identiska paket, är ett viktigt verktyg för att få bättre prestanda i videoapplikationer. Tyvärr är det inbyggda stödet för multicast i den viktiga IEEE 802.11 standardfamiljen för trådlösa nätverk mycket outvecklat. Ettstort problem är att det inte går att veta om ett paket har kommit fram eller om det har försvunnit i en, mycket trolig, krock. Vi har vidareutvecklat och anpassat en föga känd krockdetektionsmekanism från 80-talet för använding i IEEE 802.11 nätverk. Den anpassade algoritmen kallar vi EMCD vilket är en förkortning för ‘‘Early Multicast Collision Detection’’ eller tidig krockupptäckt för multicast. Vi har presenterat en nysannolikhetsbaserad modell för att beräkna algoritmens prestanda undermaximal belastning. Modellen som har verifierats genom simuleringar kanäven användas för att beräkna optimala parametrar för algoritmen. Algoritmen har visats kraftigt reducera risken för oupptäckta kollisioner och reducerar den tid som går åt för dem. EMCD-algoritmen inspirerade till att utveckla en ytterligare algoritm som inte bara kan upptäcka utan också undvika kollisioner: PREMA som står för ‘‘Prioritized Repeated Eliminations Multiple Access’’ eller prioriterad kanal-åtkomst med upprepade eliminationer. Det finns två viktiga skillnader mellanhur de fungerar. I EMCD bygger kollisionsdetektionen på rektangelfördelade slumptal och en enda upptäcktsomgång. I PREMA används i stället geometriskt fördelade slumptal och upprepade omgångar. Effekten blir att man med stor säkerhet får en enda vinnare. även för PREMA presenteras en sannolikhetskalkylsbaserad prestandaanalys för maxlastfallet vilken stöds av simuleringar. Samma formler kan användas för att approximativt skatta prestanda i EY-NPMA som är en närliggande algoritm. Den var tänkt att använda i Hiperlan/1; en standard som aldrig fick något kommersiellt genombrott. Använder man den modell som vi presenterar i avhandlingens sista studiekan man med ganska god noggrannhet beräkna optimala parametrar för EY-NPMA med en beräkningsinsats O(mY S) mot O(mES×mY S) för tidigare kända algoritmer. / Imagine a taxi driver wanting to watch a football game while working. Events in the game cannot be predetermined, the driver's available attentional resources vary and network connections change from non-existing to excellent, so it will be necessary to develop a viewing application that can adapt to circumstances. This thesis presents a system model and sketches a framework for design and run time adaptations. The model has three layers: user/usage, application and network. Quality of service metrics are proposed for each layer. A particular emphasis is placed on the difference between the user/usage layer and the application layer. Satisfaction at the former means a job well done, a match played to your liking etc. Satisfaction at the latter means good picture quality, nice colours etc. The thesis continues by identifying and describing elements required to build the system used by the taxi driver. Three studies are presented where either bandwidth or delay are varied at the network level. Video is better the higher the bandwidth; animations can be used as a complement. They are shown to be better than low quality video but worse than high quality video for watching a football game. Better video in the form of higher frame rates turned out to be worse for playing a card game over the Internet. A possible explanation is the distraction experienced when the image is updated constantly. Another result of our studies is that users can adapt their mental effort to the actual load when given feedback on the network delay affecting a computer game. The results mentioned above show that it is possible to compensate for poor network performance. For the user, improved network performance is generally more satisfactory. Early multicast collision detection is a method for improved multicast performance in high load IEEE 802.11 networks. Prioritised repeated eliminations multiple access is a method for multicast and other traffic which can be used alone or in an IEEE 802.11 network. Probabilistic performance analysis and simulations show that both protocols drastically reduce the time spent in collisions and improve throughput compared to IEEE 802.11. Some of the formulae are applied to EY-NPMA as well; they are used to estimate performance and to estimate optimal operating parameters more efficiently than with previously known methods.

This research investigated the Mission Specialist role in micro unmanned aerial systems (mUAS) and was informed by human-robot interaction (HRI) and technology findings, resulting in the design of an interface that increased the individual performance of 26 untrained CBRN (chemical, biological, radiological, nuclear) responders during two field studies, and yielded formative observations for HRI in mUAS. Findings from the HRI literature suggested a Mission Specialist requires a role-specific interface that shares visual common ground with the Pilot role and allows active control of the unmanned aerial vehicle (UAV) payload camera. Current interaction technology prohibits this as responders view the same interface as the Pilot and give verbal directions for navigation and payload control. A review of interaction principles resulted in a synthesis of five design guidelines and a system architecture that were used to implement a Mission Specialist interface on an Apple iPad. The Shared Roles Model was used to model the mUAS human-robot team using three formal role descriptions synthesized from the literature (Flight Director, Pilot, and Mission Specialist). The Mission Specialist interface was evaluated through two separate field studies involving 26 CBRN experts who did not have mUAS experience. The studies consisted of 52 mission trials to surveil, evaluate, and capture imagery of a chemical train derailment incident staged at Disaster City. Results from the experimental study showed that when a Mission Specialist was able to actively control the UAV payload camera and verbally coordinate with the Pilot, greater role empowerment (confidence, comfort, and perceived best individual and team performance) was reported by a majority of participants for similar tasks; thus, a role-specific interface is preferred and should be used by untrained responders instead of viewing the same interface as the Pilot in mUAS. Formative observations made during this research suggested: i) establishing common ground in mUAS is both verbal and visual, ii) type of coordination (active or passive) preferred by the Mission Specialist is affected by command-level experience and perceived responsibility for the robot, and iii) a separate Pilot role is necessary regardless of preferred coordination type in mUAS. This research is of importance to HRI and CBRN researchers and practitioners, as well as those in the fields of robotics, human-computer interaction, and artificial intelligence, because it found that a human Pilot role is necessary for assistance and understanding, and that there are hidden dependencies in the human-robot team that affect Mission Specialist performance.

The Traffic alert and Collision Avoidance System (TCAS) is an aircraft collision avoidance system designed to prevent mid-air collisions. During an advisory, danger is imminent, and TCAS is assumed to have better, more up-to-date information than the ground operated air traffic control (ATC) facility. Following a TCAS RA is generally the safe course of action during an advisory. However, pilot compliance with RAs is surprisingly low. Results from a TCAS monitoring study show pilots are not complying with many TCAS advisories. As revealed by pilot-submitted Aviation Safety Reporting System (ASRS) reports, this noncompliance could be attributed, in part, to pilot confusion to TCAS operation as well as misunderstandings of the appropriate response to a TCAS issued advisory. This thesis details the development and evaluation of a TCAS training program intended to improve pilots' understanding of TCAS use for collision avoidance in a range of traffic situations. The training program integrated Demonstration Based and Event Based Training techniques. Its efficacy was analyzed in an integrated ATC-cockpit simulator study in which eighteen commercial airline pilots were asked to complete the TCAS training program and afterwards experienced twelve experimental traffic events. The trained pilots' performance was compared to the performance of 16 baseline pilots who did not receive the modified training. Overall, the training program did have a significant impact on the pilots' behavior and response to TCAS advisories. The measure Time Pilots First Achieved Compliance decreased with the trained pilots, as did the measure Autopilot Disconnect Time After RA Initiation. Trained pilots exhibited less aggressive performance in response to a TCAS RA (including a decrease in the measures Altitude Deviation Over Duration Of RA, Average Vertical Rate Difference, Maximum Vertical Rate Difference, and Maximum Vertical Rate). The measure Percent Compliance did not significantly vary between trained and baseline pilots, although trained pilots had a more consistent response in the traffic event with conflicting ATC guidance. Finally, on the post-experiment questionnaires, pilots commented on their increase in understanding of TCAS as well as an increase in their trust in the advisory system. Results of this research inform TCAS training objectives provided by the FAA as well as the design of TCAS training. Additionally, conclusions extend more broadly to improved training techniques for other similar complex, time-critical situations.

Large-scale voting usability problems have changed the outcomes of several recent elections. The 2006 election in Sarasota County, Florida was one such incident, where the number of votes lost was nearly 50 times greater than the margin of victory for the US Representative race. Multiple hypotheses were proposed to explain this incident, with prevailing theories focused on malicious software, touchscreen miscalibration or poor ballot design, Study I aimed to empirically determine whether Sarasota voters unintentionally skipped the critical US Representative race due to poor ballot design. The Sarasota ballot was replicated initially, then header highlighting and number of races presented on the first screen were manipulated. While the presentation of multiple races had a significant effect on undervotes in the US Representative race, header highlighting did not. Nearly 20% of all voters (27 of 137) skipped the race their first time on that screen, an even greater undervote rate than that originally seen in Sarasota. In conjunction with other research, Study I results strongly suggests that the 2006 Sarasota election was almost certainly a human factors problem. A cognitive model of human voters was developed based on Study I data. Model predictions were then compared with behavioral data from Study 2, in which participants voted on a replica of the Charlotte County, Florida 2006 ballot.

Due to the varieties in knowledge advancements nowadays, the concepts of control room have been created in different directions. These concepts have been implemented in various fields of business to increase advantages over competitors. Meanwhile, an individual company has its own experiences and strategies, the results in the actual implementation are different to the conceptual plan. Most of them were not qualified to the actual specification and expectation. These exposures lead this research to find out and discuss about the proper criteria to improve the control centers’ performance. The viewpoints used in this study are discussed based on the perspectives of control system designers, operators, and researchers to create validity for the analysis and conclusion. The final result of this research can be used to generate realization in the necessities of improving control centers to support the operators’ performances to create competitive advantages for business sectors. In conclusion, this research aspires to be used as a guideline for the control centers design and improvement strategies to increase their performance and productivities.

Generic airspace, or air traffic control sectors with similar operational characteristics, is an operational concept being proposed as a means of increasing staffing flexibility and reducing training times as part of the Federal Aviation Administration’s (FAA’s) Next Generation (NextGen) air traffic control (ATC) modernization efforts. A key need for implementing generic airspace is identifying groups of similar sectors with respect to training required for controllers to make transitions between those sectors. Through the development and validation process of the studies performed in this thesis, a structure-based classification scheme was found to be an effective way to classify sectors in order to support a minimal differences training approach to generic airspace. The resulting classes of sectors are expected to have fewer transition barriers and support increased staffing flexibility. In order to assess similarities of airspace sectors, factors affecting how easily a controller makes a transition from one sector to another were identified using semi-structured interviews with experienced air traffic controllers. The most important factors appear to reflect familiarity with types of operations and common traffic patterns, providing a basis for classifying groups of sectors. The controllers identified some techniques that are easily transferable as well. Some factors that are very specific to transitions were identified as well, such as “knowing the neighbor sectors” and “coastal area” factors. Based on the most important factors, traffic patterns in 404 high-altitude National Airspace System (NAS) sectors were examined for common traffic patterns. These traffic patterns were used as the basis for two classification approaches, a holistic classification approach and a decompositional classification approach. These approaches are used to classify current air traffic control sectors into classes with common structural characteristics. The results identify existing sectors with near-term potential as being generic sectors that support a minimal differences training approach to generic airspace. Further analysis with the sector classification results identified that the number of factors incorporated in the classification methods are directly associated with the method's effectiveness. In order to examine the validity of the developed classification methods and to assess the relative importance of the factors involving transitions identified by the interviews, an online survey was conducted with 56 air traffic controllers. The results indicated that the classification methods developed support controllers' perception of airspace similarities. Some qualitative data gained from the survey provides an insightful aspect for future steps continuing this study such as additional important factors to be considered. Some of these factors are considered as part of the classification schemes developed in this thesis while some are yet to be incorporated. Some of these additional factors were found to be more feasible to be incorporated into future classification schemes than other factors.

Can a robot understand a human's social behavior? Moreover, how should a robot act in response to a human's behavior? If the goals of artificial intelligence are to understand, imitate, and interact with human level intelligence then researchers must also explore the social underpinnings of this intellect. Our endeavor is buttressed by work in biology, neuroscience, social psychology and sociology. Initially developed by Kelley and Thibaut, social psychology's interdependence theory serves as a conceptual skeleton for the study of social situations, a computational process of social deliberation, and relationships (Kelley&Thibaut, 1978). We extend and expand their original work to explore the challenge of interaction with an embodied, situated robot. This dissertation investigates the use of outcome matrices as a means for computationally representing a robot's interactions. We develop algorithms that allow a robot to create these outcome matrices from perceptual information and then to use them to reason about the characteristics of their interactive partner. This work goes on to introduce algorithms that afford a means for reasoning about a robot's relationships and the trustworthiness of a robot's partners. Overall, this dissertation embodies a general, principled approach to human-robot interaction which results in a novel and scientifically meaningful approach to topics such as trust and relationships.

This body of work explores an emerging aspect of human-robot interaction, transparency. Socially guided machine learning has proven that highly immersive robotic behaviors have yielded better results than lesser interactive behaviors for performance and shorter training time. While other work explores this transparency in learning by demonstration using non-verbal cues to point out the importance or preference users may have towards behaviors, my work follows this argument and attempts to extend it by offering cues to the internal task representation. What I show is that task-transparency, or the ability to connect and discuss the task in a fluent way implores the user to shape and correct the learned goal in ways that may be impossible by other present day learning by demonstration methods. Additionally, some participants are shown to prefer task-transparent robots which appear to have the ability of "introspection" in which it can modify the learned goal by other methods than just demonstration.

In a home environment, there are many tasks that a human may need to accomplish. These activities, which range from picking up a telephone to clearing rooms in the house, all have the common trend of fetching. These tasks can only be completed correctly with the consideration of many things including an understanding of what the human wants, recognition of the correct item from the environment, and manipulation and grasping of the object of interest. The focus of this work is on addressing one aspect of this problem, decomposing an image scene such that a task-specific object of interest can be identified. In this work, communication between human and robot is represented using a feedback formalism. This involves the back-and-forth transfer of textual information between the human and the robot such that the robot receives all information necessary to recognize the task-specific object of interest. We name this new communication mechanism Interactive Text Response (ITR), which we believe will provide a novel contribution to the field of Human Robot Interaction. The methodology employed involves capturing a view of the scene that contains an object of interest. Then, the robot makes inquiries based on its current understanding of the scene to disambiguate between objects in the scene. In this work, we discuss development of ITR in human-robot interaction, and understanding of variability, ease of recognition, clutter, and workload needed to develop an interactive robot system.