WNCG - Wireless Networking and Communications Group - Evdokia Nikolova
https://wncg.org/tags/evdokia-nikolova
enThe Burden of Risk Aversion in Selfish Routing
https://wncg.org/research/briefs/burden-risk-aversion-selfish-routing
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Wednesday, April 8, 2015</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>Traffic congestion aggravates the daily life of millions of people around the globe and congestion games from game theory provide a suitable tool to understand its effects and offer insights on how to alleviate it. Classic congestion games assume deterministic edge delays, while in reality delays are uncertain and risk-averse drivers might prefer longer but safer routes, further exacerbating the problem of increased travel times and emissions.</p>
<p>Considering congestion games with uncertain delays, WNCG faculty Evdokia Nikolova and collaborator Nicolas Stier-Moses compute the inefficiency introduced in network routing by risk-averse users. At equilibrium, users may select paths that do not minimize the expected delay so as to obtain lower variability. A social planner, who is likely to be more risk neutral than agents because it operates at a longer time-scale, quantifies social cost with the total expected delay along routes. From that perspective, users may make suboptimal decisions that degrade long-term quality. </p>
<p>Nikolova and Stier-Moses define the price of risk aversion as a way to quantify the price users pay in terms of extra travel time due to their risk-averse routing choices. Formally, it is the worst-case ratio of the social cost at a risk-averse equilibrium to that where users are risk-neutral. For networks with general delay functions and a single origin-destination pair for all users, they show that the price of risk-aversion depends linearly on the users' risk tolerance and on the degree of variability present in the network. They obtain this result via a combinatorial proof that employs alternating paths that are reminiscent of those used in max-flow algorithms. For series-parallel graphs, the price of risk-aversion becomes independent of the network topology and its size. As a result of independent interest, they also prove that for series-parallel networks with deterministic edge delays, the equilibrium maximizes the shortest-path objective among all feasible flows. </p>
<p>This work also paves the ground for examining ways in which risk-aversion can help reduce rather than worsen congestion, where risk-aversion may be leveraged to spread traffic in the place of tolls. </p>
<p><strong>The Burden of Risk Aversion in Mean-Risk Selfish Routing</strong> </p>
<p>Evdokia Nikolova, Nicolas E. Stier Moses.</p>
<p>November, 2014.</p>
<p><a href="http://arxiv.org/abs/1411.0059">http://arxiv.org/abs/1411.0059</a></p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/evdokia-nikolova">Evdokia Nikolova</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/evdokia-nikolova">Evdokia Nikolova</a>, <a href="/tags/traffic-routing">Traffic routing</a>, <a href="/tags/wncg">WNCG</a></div></div></div>Wed, 08 Apr 2015 16:24:05 +0000lab27993715 at https://wncg.orghttps://wncg.org/research/briefs/burden-risk-aversion-selfish-routing#commentsEfficient Computation of Adaptive Routing Decisions Under Uncertainty
https://wncg.org/research/briefs/efficient-computation-adaptive-routing-decisions-under-uncertainty
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Wednesday, April 8, 2015</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>A central question in decision-making under uncertainty is how to mitigate risk. Under the expected utility framework this means to find the decision (or a sequence of decisions) that maximizes the expected utility of the decision, for some nonlinear utility function that represents the agent’s risk-averse preferences. In sequential decision-making, due to the large space of possible decisions, it is especially challenging to compute an optimal policy—that is, an optimal sequence of actions, efficiently. In the prevalent framework of Markov Decision Processes (MDPs), efficient algorithms are known for the special cases of linear utility (that corresponds to a risk-neutral agent) and exponential utility. Little is known about efficient computation of the optimal policy for utility functions beyond linear and exponential, and PhD student Darrell Hoy and WNCG faculty Evdokia Nikolova provide examples suggesting that computing the optimal policy in such situations is likely intractable. With the goal of retaining efficient computation, they focus on finding an approximately optimal policy and, in particular, on the question how well can we approximate routing decisions in polynomial time?</p>
<p> Hoy and Nikolova establish that the optimal routing policy can be approximated arbitrarily well, in polynomial time, under general monotone utility functions that can capture a wide range of risk-averse attitudes of the decision maker. </p>
<p> At the core of their algorithm they present a general-purpose technique based on adaptive discretization that works for any monotone utility function. The main insight is to perform discretization at the utility level space, which results in a nonuniform discretization of the domain (cost or travel time). In contrast to much existing research, this approximation is to the optimal continuous-time policy, not the optimal policy to the time-discretized problem. This is essential in nonlinear utility models since standard constant-step discretization of time incurs approximation errors that may become significant when the utility function is a step function (e.g., the probability of arriving on time) or a steep function with a large gradient. Hoy and Nikolova's result represents agents who have non-linear utilities over arrival times, and their model applies equally well when agents have a non-linear utility over the expenditure of a different resource, for instance money or fuel.</p>
<p><strong>Approximately Optimal Risk-averse Routing Policies via Adaptive Discretization.</strong></p>
<p>Darrell Hoy and Evdokia Nikolova.</p>
<p>In Proceedings of the <em>Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15).</em> Austin, TX, January 25-30, 2015. </p>
<p><a href="http://darrellhoy.com/papers/AAAI2015_HoyNikolova.pdf">http://darrellhoy.com/papers/AAAI2015_HoyNikolova.pdf</a></p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/evdokia-nikolova">Evdokia Nikolova</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/evdokia-nikolova">Evdokia Nikolova</a>, <a href="/tags/darrell-hoy">Darrell Hoy</a>, <a href="/tags/adaptive-routing-decisions">Adaptive Routing Decisions</a>, <a href="/tags/wncg">WNCG</a></div></div></div>Wed, 08 Apr 2015 16:22:50 +0000lab27993714 at https://wncg.orghttps://wncg.org/research/briefs/efficient-computation-adaptive-routing-decisions-under-uncertainty#commentsProf. Evdokia Nikolova joins WNCG
https://wncg.org/news/prof-evdokia-nikolova-joins-wncg
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>WNCG welcomes new Assistant Professor Dr. Evdokia Nikolova to its lineup of world renowned faculty researchers. </p>
<p>Prof. Nikolova is the winner of a 2014 NSF CAREER Award and a 2013 Google Research Award. Her research on traffic-aware routing has been adapted in a vehicular cyber-physical system project CarTel at MIT.</p>
<p>Dr. Nikolova comes to the WNCG from Texas A&M University’s Department of Computer Science and Engineering. Prior to her time at TAMU, Prof. Nikolova received a BA in mathematics and economics from Harvard, an MS in mathematics from Cambridge University in the U.K. and a Ph.D. in computer science from MIT, where she later served as a postdoctoral associate.</p>
<p>“Part of the attraction of this faculty position is the freedom we have in choosing projects,” Nikolova states. “The WNCG is a vibrant group, and I was attracted to the opportunities for interdisciplinary research and the many potential collaborators in the group.” </p>
<p>Nikolova joined UT Austin and the WNCG in January 2014 and will expand on her research that focuses on risk mitigation for networks using algorithms and game theory, specifically in the areas of transportation and energy. She looks forward to participating in collaborative research efforts with faculty and students across disciplines who share mutual research interests.</p>
<p>“I love that the WNCG really emphasizes collaboration. It has a strong collegial spirit that helps the faculty and students both professionally and socially,” Prof. Nikolova states. “There’s a real sense of cohesion in the group. We like each other and we enjoy discussing research and working with each other so it’s great on many levels.”</p>
<p>While already teaching at UT, Prof. Nikolova looks forward to advising Ph.D. students coming in the Fall 2014 semester. She is currently searching for postdoctoral associates who cross boundaries with interdisciplinary studies closely related to her areas of interest.</p>
</div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-above"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/evdokia-nikolova">Evdokia Nikolova</a></div><div class="field-item odd"><a href="/tags/new-faculty">new faculty</a></div></div></div><div class="field field-name-field-publish-date field-type-datetime field-label-above"><div class="field-label">Publish Date: </div><div class="field-items"><div class="field-item even"><span class="date-display-single">Wednesday, April 23, 2014</span></div></div></div><div class="field field-name-field-image field-type-image field-label-above"><div class="field-label">Key Image: </div><div class="field-items"><div class="field-item even"><img src="https://wncg.org/sites/wncg.org/files/Evdokia%2520Nikolova_0.jpg" width="350" height="250" /></div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-above"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/evdokia-nikolova">Evdokia Nikolova</a></div></div></div><div class="field field-name-field-feature field-type-list-boolean field-label-above"><div class="field-label">Feature: </div><div class="field-items"><div class="field-item even">No</div></div></div>Wed, 23 Apr 2014 15:22:18 +0000lab27993440 at https://wncg.orghttps://wncg.org/news/prof-evdokia-nikolova-joins-wncg#comments