133 research outputs found
Wardrop Equilibrium Can Be Boundedly Rational: A New Behavioral Theory of Route Choice
As one of the most fundamental concepts in transportation science, Wardrop
equilibrium (WE) has always had a relatively weak behavioral underpinning. To
strengthen this foundation, one must reckon with bounded rationality in human
decision-making processes, such as the lack of accurate information, limited
computing power, and sub-optimal choices. This retreat from behavioral
perfectionism in the literature, however, was typically accompanied by a
conceptual modification of WE. Here we show that giving up perfect rationality
need not force a departure from WE. On the contrary, WE can be reached with
global stability in a routing game played by boundedly rational travelers. We
achieve this result by developing a day-to-day (DTD) dynamical model that
mimics how travelers gradually adjust their route valuations, hence choice
probabilities, based on past experiences. Our model, called cumulative logit
(CULO), resembles the classical DTD models but makes a crucial change: whereas
the classical models assume routes are valued based on the cost averaged over
historical data, ours values the routes based on the cost accumulated. To
describe route choice behaviors, the CULO model only uses two parameters, one
accounting for the rate at which the future route cost is discounted in the
valuation relative to the past ones and the other describing the sensitivity of
route choice probabilities to valuation differences. We prove that the CULO
model always converges to WE, regardless of the initial point, as long as the
behavioral parameters satisfy certain mild conditions. Our theory thus upholds
WE's role as a benchmark in transportation systems analysis. It also resolves
the theoretical challenge posed by Harsanyi's instability problem by explaining
why equally good routes at WE are selected with different probabilities
Decisions, decisions, decisions: the development and plasticity of reinforcement learning, social and temporal decision making in children
Human decision-making is the flexible way people respond to their environment, take actions, and plan toward long-term goals. It is commonly thought that humans rely on distinct decision-making systems, which are either more habitual and reflexive or deliberate and calculated. How we make decisions can provide insight into our social functioning, mental health and underlying psychopathology, and ability to consider the consequences of our actions. Notably, the ability to make appropriate, habitual or deliberate decisions depending on the context, here referred to as metacontrol, remains underexplored in developmental samples. This thesis aims to investigate the development of different decision-making mechanisms in middle childhood (ages 5-13) and to illuminate the potential neurocognitive mechanisms underlying value-based decision-making. Using a novel sequential decision-making task, the first experimental chapter presents robust markers of model-based decision-making in childhood (N = 85), which reflects the ability to plan through a sequential task structure, contrary to previous developmental studies. Using the same paradigm, in a new sample via both behavioral (N = 69) and MRI-based measures (N = 44), the second experimental chapter explores the neurocognitive mechanisms that may underlie model-based decision-making and its metacontrol in childhood and links individual differences in inhibition and cortical thickness to metacontrol. The third experimental chapter explores the potential plasticity of social and intertemporal decision-making in a longitudinal executive function training paradigm (N = 205) and initial relationships with executive functions. Finally, I critically discuss the results presented in this thesis and their implications and outline directions for future research in the neurocognitive underpinnings of decision-making during development
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 usersâ 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
Pro-Environmental Behaviour Change for Nature: Empirical and Theoretical Evidence from a Field Experiment in Aotearoa New Zealand
Individual behaviour change is a crucial component of our response to current environmental challenges and over recent years, a growing body of literature has focussed on the drivers and levers of pro-environmental behaviours. However, scholars have noted there is a considerable shortage of behavioural science research that focuses on behaviours that directly impact nature and biodiversity. This is concerning, given the enormous value populations place on nature, the fundamental role nature plays in society and because nature is declining rapidly.
In this thesis, we focus on understanding volunteering for nature restoration groups, which we show is an under-researched behaviour in the literature. It also has relatively high potential to deliver positive impacts for biodiversity and nature. We start by developing a simple generalisable theoretical model that suggests three main factors may be inhibiting the uptake of volunteering for nature â uncertainty, inaccuracy and high behavioural adjustment costs. We use this model to inform the design and hypotheses for a large field experiment in Aotearoa New Zealand where we aim to answer the following questions:
How can we increase volunteering for nature restoration groups? What are the effects of volunteering for the first-time on future volunteering behaviour? How does volunteering affect other important outcomes of interest, like environmental identity, locus of control beliefs and wellbeing?
Our field experiment has two stages. In stage one, we randomly assign first-time volunteers (those who are not already engaged in nature volunteering) to treatment groups to assess the impact of a nudge, a supermarket voucher incentive and a nudge and incentive combined on volunteering behaviour. We find that a $50 NZD supermarket incentive increases attendance rates at volunteering events and commitment rates to attend volunteering events. On the other hand, an environmentally and socially motivated nudge in isolation has no effect on volunteering behaviour. However, combining the nudge with the voucher incentive enhances the efficacy of either treatment alone, demonstrating that significant positive synergies exist between nudges and incentives in this context.
In stage two, we show volunteering for the first-time is plausibly randomly assigned, conditional on availability and being offered an incentive. We use this feature to estimate the causal impact of volunteering for the first time on future volunteering behaviour and other outcomes of interest. We find that volunteering for the first time crowds in future volunteering behaviour, generates positive spillovers to other pro-environmental behaviours and strengthens environmental self-identity and locus of control beliefs, which are important pre-cursors to pro-environmental behaviour. Our results show two mechanisms are likely driving these effects. Firstly, volunteering for the first-time provides important information about the benefits of volunteering that are used in future decision-making. Secondly, it strengthens environmental attitudes and identity, which in-turn affect preferences for pro-environmental behaviour. Taken together, our results show that using a financial incentive to help people experiment with volunteering can lead to large positive spillovers and crowding-in effects for future pro-environmental behaviour
Survey of Human Models for Verification of Human-Machine Systems
We survey the landscape of human operator modeling ranging from the early
cognitive models developed in artificial intelligence to more recent formal
task models developed for model-checking of human machine interactions. We
review human performance modeling and human factors studies in the context of
aviation, and models of how the pilot interacts with automation in the cockpit.
The purpose of the survey is to assess the applicability of available
state-of-the-art models of the human operators for the design, verification and
validation of future safety-critical aviation systems that exhibit higher-level
of autonomy, but still require human operators in the loop. These systems
include the single-pilot aircraft and NextGen air traffic management. We
discuss the gaps in existing models and propose future research to address
them
Socio-hydrology from Local to Large Scales: An Agent-based Modeling Approach
For decades, the interaction between water and people has attracted hydrologistsâ attention. However, the coevolution of social and natural processes, which occurs across a range of time scales, has not yet been adequately characterized. This research gap has motivated more research in recent years under the umbrella of âsocio-hydrologyâ. The purpose of socio-hydrology is to posit the endogeneity of humans in a hydrological system and then to investigate feedback mechanisms between hydrological and human systems that might lead to emergent phenomena.
The current state-of-the-art in socio-hydrology faces several challenges that include (1) a tenuous connection of socio-hydrology to broader research on social, economic, and policy aspects of water resources, (2) the (in)capability of socio-hydrological models to capture human behavior by generic feedback mechanisms that can be extrapolated to other places, and (3) unsatisfying calibration or validation processes in modeling. To address the first gap, a socio-hydrology study needs to connect proper social theories on water-related human decision making with a water resource model based on a given context and scale. Addressing the second gap calls for socio-hydrology research with case studies in different and contrasting regions and at different scales. In fact, such study can shed light on the similarities and differences in socio-hydrological systems in different contexts and scales as initial steps for future research. The third research gap calls for a socio-hydrology study that improves calibration and validation processes. Thus, to address all these gaps in one thesis, two case studies with completely different environments are chosen to investigate various phenomena at different scales.
The research presented here contributes to socio-hydrological understanding at two spatial scales. To account for the heterogeneity of human decision making and its interactions with the hydrologic system, an agent-based modeling (ABM) approach is used in this research. The first objective is to explore human adaptation to drought as well as the subsequent expected or unexpected effects on the agricultural sector and to develop a socio-hydrological model to predict agricultural water demand. To do so, an agent-based agricultural water demand model (ABAD) is developed. This model is applied to the Bow River Basin in Alberta, Canada, as a study region, which has recently experienced drought periods. The second objective is to explore conflict-and-cooperation processes in transboundary rivers as socio-hydrological phenomena at a large scale. The Eastern Nile Basin Socio-hydrological (ENSH) model is developed and applied to the Eastern Nile Basin (ENB) in Africa in which conflict-and-cooperation dynamics can be seen among Egypt, Sudan, and Ethiopia. The ENSH model aims to quantify and simulate these countriesâ willingness to cooperate in the ENB.
ABAD demonstrates (1) how farmersâ attitudes toward profits, risk aversion, environmental protection, social interaction, and irrigation expansion explain the dynamics of the water demand and (2) how the conservation program may paradoxically lead to the rebound phenomenon whereby the water demand may increase after decreasing through modernized irrigation systems. Through the ABAD model analysis, economic factors are found to dominantly control possible rebounds. Based on the insights gained via the model analysis, it is discussed that several strategies, including community participation and water restrictions, can be adopted to avoid the rebound phenomenon in irrigation systems. Fostering farmersâ awareness about the average water use in their community could be a means to avoid the rebound phenomenon through community participation. Also, another strategy to avoid the rebound phenomenon could be to reassign water allocations to reduce farmersâ water rights.
The ENSH model showed that (1) socio-political factors (i.e., relative political stability and foreign direct investment) can explain two historical trends (i.e., (a) fluctuations in Ethiopiaâs willingness to cooperate between 1983 and 2009 and (b) a decreasing Ethiopiaâs willingness to cooperate between 2009 and 2016); (2) the 2008 food crisis (i.e., Sudanâs food gap) may account for Sudan recovering its willingness to cooperate; and (3) Egyptâs political (in)stability plays a role in its willingness to cooperate.
The outcomes of this research can provide valuable insights to support policymakers for the long-term sustainability of water planning. This research investigates two main socio-hydrological phenomena at different spatial scales: the agricultural rebound phenomenon at a small geographical scale and the conflict and cooperation phenomena at a large geographical scale. The emergence of these phenomena can be a complex resultant of interaction and feedback mechanisms between the social system at the individual, institutional, and society levels and the hydrological system. Through developing quantitative socio-hydrological models, this research investigates the feedback mechanisms that may lead to the rebound phenomenon at a small scale and the conflict and cooperation phenomenon at a large scale. Finally, the research shows how these socio-hydrological models can be used for sustainable water management to avoid negative long-term consequences
Computational and cognitive mechanisms of exploration heuristics
Should I leave or stay in academia? Many decisions we make require arbitrating between novelty and the benefits of familiar options. This is called the exploration-exploitation trade-off. Solving this trade-off is not trivial, but approximations (called âexploration strategiesâ) exist. Humans are known to rely on different exploration strategies, varying in performance and computational requirements. More complex strategies perform well, but are computationally expensive (e.g., require to compute
expected values). Cheaper strategies, i.e., heuristics, require less cognitive resources but can lead to sub-optimal performance. The simplest heuristic strategy is to ignore prior knowledge, such as expected values, and to choose entirely randomly. In effect, this is like rolling a dice to choose between different choice options. Such âvalue-free randomâ exploration strategy may not always lead to optimal performance but allows to spare cognitive resources. In this thesis, I investigate the mechanisms of exploration heuristics in human decision
making. I developed a cognitive task allowing to dissociate between different strategies for exploration. In my first study, I demonstrate that humans supplement
complex strategies with exploration heuristics and, using a pharmacological manipulation, that value-free random exploration is specifically modulated by the neurotransmitter noradrenaline. Exploration heuristics are of particular interest when access to cognitive resources is limited and prior knowledge uncertain, such as in development and mental health disorders. In a cross-sectional developmental study, I demonstrate that value-free random exploration is used more at a younger age. Additionally, in a large-sample online study, I show that it is specifically associated to impulsivity. Together, this indicates that value-free random exploration is useful in certain contexts (e.g., childhood) but that high levels of it can be detrimental. Overall, this thesis attempts to better understand the process of exploration in humans, and opens the way for understanding the mechanisms of arbitration between complex and simple strategies for decision making
Neues aus Wissenschaft und Lehre der Heinrich-Heine-UniversitĂ€t DĂŒsseldorf 2010
Das Jahrbuch der Heinrich-Heine-UniversitĂ€t DĂŒsseldorf versteht sich als Forum fĂŒr den wissenschaftlichen Dialog der UniversitĂ€t zu Zeitfragen, zu aktuellen Problemlagen und Herausforderungen von Wissenschaft und Gesellschaft, als BrĂŒcke der Vermittlung zwischen Forschung und Ăffentlichkeit sowie als GedĂ€chtnisort der Innovationen und des Fortschritts in Forschung und Lehre der UniversitĂ€t und als Speicher der wissenschafts- und hochschulpolitischen Entscheidungen fĂŒr strukturelle Weichenstellungen mit Langzeitwirkung. Zielgruppe ist die an den Arbeitsergebnissen in Forschung und Lehre sowie an wissenschaftlichen Entscheidungen der Heinrich-Heine-UniversitĂ€t interessierte Ăffentlichkeit. Diese soll ĂŒber die Dynamik und das sich wandelnde Profil der FakultĂ€ten kontinuierlich informiert und in die Lage versetzt werden, sich intensiver mit neuen Forschungsfragen und -ergebnissen auseinander zu setzen. Es geht vor allem darum, die Bedeutung der Forschung fĂŒr die verschiedenen Lebensbereiche und damit auch fĂŒr unsere gesellschaftliche Entwicklung bewusst zu machen. Die BeitrĂ€ge vermitteln gleichsam als Momentaufnahme einen Ausschnitt aus dem permanenten Prozess des sich verĂ€ndernden Profils der FakultĂ€ten. Erst eine Folge von JahrbĂŒchern eröffnet die Chance, die Tiefe des Gesamtprofils auszuloten und dessen Nachhaltigkeit zu erkennen
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