Applied

The Causes of Crime and the Practical Limits of Crime Control

Speaker:

Jeff Brantingham

Date:

Wed, Sep 19, 2012

Location:

IRMACS Center, Simon Fraser University

Conference:

Hot Topics in Computational Criminology

Abstract:

Within criminology there continues to be wide disagreement over the importance the individual, formal and informal social structure and the environment in driving crime patterns. In person-based theories, individuals are assumed to either innately possess the capacity to commit crime, or learn such capacities from their interactions with others. In structural theories, it is generally assumed that individuals are constrained by static social, economic or political organization, which makes crime a necessary or acceptable alternative to non-crime activities. In environmental theories, the built environment creates abundant, if unevenly distributed opportunities for crime that are easily exploited. While each of these theoretical perspectives finds some justification in empirical studies, they are not equal practical from the point of view of crime control. This talk will review several key ideas underlying crime and crime pattern formation and argue in favor of modeling of short-term, local crime processes because it is these processes that are most easily disrupted and are likely to yield practical results.

Class:

Subject:

Mathematics of Crime

Speaker:

Andrea L. Bertozzi

Date:

Wed, Sep 19, 2012

Location:

IRMACS Center, Simon Fraser University

Conference:

Hot Topics in Computational Criminology

Abstract:

There is an extensive applied mathematics literature developed for problems in the biological and physical sciences. Our understanding of social science problems from a mathematical standpoint is less developed, but also presents some very interesting problems. This lecture uses crime as a case study for using applied mathematical techniques in a social science application and covers a variety of mathematical methods that are applicable to such problems. We will review recent work on agent based models, differential equations, variational methods for inverse problems and statistical point process models. From an application standpoint we will look at problems in residential burglaries and gang crimes.
Examples will consider both "bottom up" and "top down" approaches to understanding the mathematics of crime, and how the two approaches could converge to a unifying theory.

Class:

Subject:

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Hot Topics in Computational Criminology

Conference:

Hot Topics in Computational Criminology

Date:

Wed, Sep 19, 2012

Class:

Subject:

Read more about Hot Topics in Computational Criminology
8336 reads

Stiffness analysis of cardiac cell models: implications on real-time heart simulation

Date:

Tue, Nov 22, 2011

Class:

Applied

Subject:

Mathematics

Optimal Investment for an Insurance Company

Speaker:

Alexandru Badescu

Location:

Calgary Place Tower (Shell)

University of Calgary, Calgary, Canada

Conference:

Shell Lunchbox Lectures

Abstract:

Optimal investment is a key problem in asset-liability management of an insurance company. Rather than allocating wealth optimally so as to maximize the overall investment return, an insurance company is interested in assessing the risk exposure where both assets and liabilities are included and minimizing the risk of mismatches between them. Different approaches for solving optimization problems by minimizing standard risk measures such as the value at risk (VaR) or the conditional value at risk (CVaR) have been proposed in the literature. In this paper we focus on some Solvency II applications by investigating several novel problems for jointly quantifying the optimal initial capital requirement and the optimal portfolio investment under various constraints.

Discussions on the convexity of these problems are also provided. Using a Monte Carlo simulation and a semi-parametric approach based on different assumptions for the loss distribution, we compute the insurer optimal capital needed to be efficiently invested in a portfolio formed by two or more assets. Finally, a detailed numerical experiment is conducted to assess the robustness and sensitivity of our optimal solutions relative to the model factors.

This paper was written in collaboration with Alexandru V. Asimit (Cass Business School, City University, UK), Tak Kuen Siu (Faculty of Business and Economics, Macquarie University, Australia)and Yuriy Zinchenko (Department of Mathematics and Statistics, University of Calgary).

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