var sms = '<div id="sms_bulletin">  <div id="sms_header">  <div class="do_not_print" id="arrow_left"><a href="?next=-35"><img src="//www2.math.ethz.ch/smsjs/images/arrow_left.png"/></a></div><div class="do_not_print" id="arrow_right"><a href="?next=-33"><img src="//www2.math.ethz.ch/smsjs/images/arrow_right.png"/></a></div>    <div class="sms_title">      <span style="float: left;">SMS</span>      <span>SWISS MATHEMATICAL SOCIETY</span>      <span style="float: right;">SMG</span>    </div>    <div class="sms_info">      <span style="float: left; width: 250px;">Bulletin no. 13</span>      <span style="float: right; text-align:right; width: 250px;">26.8 - 30.8.2024</span>      <span style="text-align: center; display: block; width: 250px; margin: auto; position: relative;">Summer Break 2024</span>    </div>  </div>  <div></div>  <div id="sms_content">    <div class="day">      <div class="day_title">        Monday, August 26      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_la"><a class="external" href="https://www.epfl.ch">EPF Lausanne</a></div><div class="event_type"><a class="external" href="https://theory.epfl.ch/seminar/">Theory and Combinatorics Seminar</a></div><br /><div class="event_speaker"> Eva Delt</div><div class="event_title">Optimizing over Fair Allocations <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />This thesis explores the problem of fair resource allocation among agents, extending theconcept of envy-freeness up to any good (EFx) by introducing a cost function that canrepresent various factors such as transportation costs, resource availability, geographicconsiderations, and social factors like income disparity. We introduce and formally definethe minCost-EFx Allocation problem, where the objective is to identify an EFx allocation that minimizes total cost. Our research investigates the algorithmic complexityof this problem, with particular emphasis on the restricted variant, minCostFactor-EFx Allocation, where the cost is agent-specific and independent of the specific itemsallocated. For this variant, we provide a polynomial-time solution for two-agent scenarios and prove NP-hardness for cases involving three or more agents. We find that the overall complexity is primarily related to the number of items, which leads us to explore parameter-restricted scenarios and show that the problem becomes more tractable when the item valuations or the number of item types is limited. Finally, we investigate approximation algorithms, establishing an inapproximability bound, and proposing agreedy algorithm that provides practical solutions for real-world applications.</div><div class="event_time">11:00 &bull; EPF Lausanne, BC 420</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Tuesday, August 27      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_ge"><a class="external" href="https://unige.ch">Université de Genève</a></div><div class="event_type"><a class="external" href="https://www.unige.ch/math/annonces/groupes-geometrie">Groupes et Géométrie </a></div><br /><div class="event_speaker"> Liran  Ron (Ben-Gurion University)</div><div class="event_title">Horofunctions and virtual homomorphisms on Groups <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />In geometric group theory, finitely generated groups are studied from the geometric properties of their Cayley graphs. This point of view sees the group as a metric space, and allows one to apply methods of metric geometry, such as the metric compactification (also called the horofunction compactification). When considering the canonical action of the group on its boundary in such compactifications, it turns out that finite orbits are closely related to virtual homomorphisms. In this talk we will go over definitions, examples, known results and open problems for current and future research.</div><div class="event_time">10:30 &bull; Université de Genève, Conseil Général 7-9, Room 1-05</div>        </div>        <div class="day_content_item normal"><div class="event_location uni_la"><a class="external" href="https://www.epfl.ch">EPF Lausanne</a></div><div class="event_type"><a class="external" href="https://www.unil.ch/dsa/en/home/menuinst/seminaires--evenements/research-seminars.html">Talks in Actuarial Mathematics </a></div><br /><div class="event_speaker"> Debbie Falden (University of Liverpool)</div><div class="event_title">Calibration of risk aversion to real pension asset allocation <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />An investor\'s risk aversion is a fundamental element in financial decision-making and preferences but lacks a standardised calibration method. We propose an approach to measure the risk aversion of an investor managing a diverse portfolio that includes pension savings, real estate, and free funds. We utilise the investor\'s real asset allocation as the optimal strategy, assuming the investor\'s preferences follow a power utility function (CRRA utility function). We calibrate a risk aversion parameter by building on Merton\'s formulas for optimal investment strategies. For pension savings, we account for the present value of future premiums, which results in an optimal investment strategy consistent with real life-cycle pension products. Realistic and stable risk aversions are calibrated by constructing a customised risky fund aligned with the investor\'s preferences. Disparities in risk aversion across financial categories are examined by certainty equivalents, and a numerical study with a real Danish pension portfolio emphasises the practical applications of our results.</div><div class="event_time">11:00 &bull; EPF Lausanne, Extranef 110</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Wednesday, August 28      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Thursday, August 29      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Friday, August 30      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_la"><a class="external" href="https://www.epfl.ch">EPF Lausanne</a></div><div class="event_type"><a class="external" href="https://theory.epfl.ch/seminar/">Theory and Combinatorics Seminar</a></div><br /><div class="event_speaker"> Jason  Hartline  (Northwestern University)</div><div class="event_title">Optimization of Scoring Rules <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br /> Scoring rules are everywhere. Any decision problem where an agent has beliefs about an unknown state and takes an action and realizes payoffs according to the action and the realized state is a scoring rule. Behavioral subjects in experiments are evaluated and rewarded according to scoring rules. Machine learning algorithms are trained and evaluated according to scoring rules.  Students\' coursework is graded according to scoring rules.This talk will introduce an objective for optimizing proper scoring rules. The objective is to maximize the increase in payoff of a forecaster who exerts a binary level of effort to refine a posterior belief from a prior belief. In this framework we characterize optimal scoring rules in simple settings, give efficient algorithms for computing optimal scoring rules in complex settings, and identify simple scoring rules that are approximately optimal. In comparison, standard scoring rules in theory and practice -- for example the quadratic rule, scoring rules for the expectation, and scoring rules for multiple tasks that are averages of single-task scoring rules -- can be very far from optimal.The talk will briefly survey some recent applications to measuring AI reliance (Guo, Wu, Hartline, Hullman 2024), quantifying calibration error of online learning algorithms (Lu, Wu 2024), and evaluating textual peer reviews (Wu, Hartline 2024).Joint work with Yingkai Li, Liren Shan, and Yifan Wu</div><div class="event_time">14:00 &bull; EPF Lausanne, BC 420</div>        </div>      </div>    </div>  </div>  <div id="sms_footer">    address: Alessandra Naldi Windler, Departement Mathematik, HG G 51.4, ETH Zurich, Raemistrasse 101, CH-8092 Zurich<br />    email: <a href="mailto:bulletin@math.ch">bulletin@math.ch</a> | phone: +41 44 632 3684 | url: <a href="https://math.ch">https://math.ch</a>    <br /><br /><img src="//www2.math.ethz.ch/smsjs/sms_logo.gif" width="150" alt="SMS/SMG Logo" title="SMS/SMG Logo" />  </div></div>';