Henri Berestycki (École des Hautes Études en Sciences Sociales, Paris)

An Epidemiological Approach to the Spreading of Riots: The Dynamics of the 2005 French Riots

Alberto Bisin (New York University)

Skewed Wealth Distributions: Theory and Empirics

Invariably across a cross-section of countries and time periods, wealth distributions are skewed to the right displaying thick upper tails, that is, large and slowly declining top wealth shares. We categorize the theoretical studies on the distribution of wealth in terms of the underlying economic factors generating skewness and thick tails. Further, we show how these mechanisms can be micro-founded by the consumption-saving decisions of rational agents in specific economic and demographic environments. Finally, we quantitatively identify the factors that drive wealth dynamics in the U.S. and are consistent with the observed skewed cross-sectional distribution of wealth and with social mobility in wealth (joint work with Jess Benhabib).

Paul J. Brantingham (University of California, Los Angeles)

The Concentration-Dynamics Tradeoff and the Causes of Crime

Recent research leaves little doubt that crime is concentrated at micro-geographic scales. Much less is known about how spatial concentration of crime and crime pattern dynamics interact. This paper examines how the concentration of crime and the stability of crime hotspots changes as a function of the spatial and temporal scale of measurement. Crime is more concentrated when measured at finer spatial and temporal scales, but also more dynamic. As the scale of measurement increases, crime becomes more diffuse but the corresponding hotspots are also more stable. This fundamental tradeoff between concentration and dynamics is law-like in its behavior. The tradeoff raises important questions about causal mechanisms and whether some scales are more important than others.

Fabio Camilli (“Sapienza” Università di Roma)

PDE Models for Multi-Agent Phenomena on Networks

Networks are pervasive in biology and social sciences. In biology, they can be observed in vegetals (leaf veins, plant roots), in animals (blood capillaries, neural networks), amoebas (protoplasmic tubes with many nuclei). In the human societies, they can be found in the study of vehicular traffic, data transmission, crowd motion, supply chains, etc.
In this talk I will present several examples of evolution processes which occur on networks and their corresponding differential models and I will discuss issues and open problems related to these models.

Emiliano Cristiani (IAC – CNR, Rome)

Multiscale Modeling of Large Self-Organizing Systems with Applications to Pedestrian and Opinion Dynamics

In this talk we will first review the basic concepts of pedestrian flows and opinion dynamics on social networks, including control and optimization issues. Successively we will focus on the potential of multiscale techniques for modeling large self-organizing systems. The need for a multiscale description stems from the fact that a purely microscopic model is in general too expensive from the computational point of view, while a purely macroscopic model does not catch the effects of one-to-one interactions, which are instead crucial for the emergence of self-organizing phenomena.

Nicola Gennaioli (IGIER – Università Bocconi)

Diagnostic Expectations and Stock Returns

We document that returns on portfolios of stocks with most optimistic analyst long-term earnings growth forecasts are substantially lower than those for stocks with the most pessimistic forecasts. We further document that firms with most optimistic forecasts have grown very fast in the past, but growth and expectations of growth subsequently revert to the mean. We develop a learning model in which analysts forecast future fundamentals based on the history of earnings growth, but beliefs are shaped by the representativeness heuristic: analysts update excessively in the direction of states of the world whose objective likelihood rises the most in light of the news. The model delivers the empirical findings we initially document. We also find support for auxiliary predictions that distinguish our model from both Bayesian learning and earlier behavioral models.

Miguel Angel Herrero (Universidad Complutense de Madrid)

The Triumph of Anarchy: Lessons from the Immune System

Consider a society consisting of a large number of individuals (about 1013, several orders of magnitude larger than current world population ) organized in hundreds of different social groups (current number of UN countries being about 200) employed in thousands of different jobs. Assume further a huge immigration rate (about 1011 new arrivals per day) coupled to a high mortality rate, of the order of 1011 each day. Such society maintains full employment, and wealth is shared by all individuals. Order is maintained by an extremely efficient police force that keeps threatening invaders (or insurrects ) at bay. What kind of government would be able to keep such society from falling apart?
In a first approximation, the answer is simple: none. The society we have summarily described is not Utopia, but our body, and no central organ of control is responsible for its dynamics (in terms of number of individuals). Its exquisitely efficient performance is an emergent property and is not centrally regulated at any powerful headquarters. We shall describe in this lecture some particularly interesting features of a subset of this complex structure, the immune system, and will shortly remark on other cell regulation properties which show a distinct emergent character as well.

Shane D. Johnson (University College London)

Graph Theory in Action: The Role of the Street Network in Crime Pattern Formation

That crime is spatially concentrated now seems uncontestable.  As such, considerable research effort has been devoted to explaining why it occurs where it does.  Researchers have examined patterns of crime using different units of analysis including large areal units, small census tracks, and more recently, street segments.  The selection of a particular unit of analysis is important for ensuring that the theoretical mechanisms of interest are adequately captured and to avoid (for example) the ecological fallacy. In this presentation, I will focus on the role of the street network in crime pattern formation.  I will discuss how the street network has been quantified using graph theory metrics, how such metrics have been used to estimate offender awareness of criminal opportunities and ambient guardianship at the street segment level, and how these have been used to test theories of crime pattern formation and offender spatial decision making.  I will consider the role of the street network in outdoor serious violence, drug offences and residential burglary using empirical examples.

Francesco Salvarani (Università di Pavia)

Kinetic Games

In this talk, we consider a population of rational individuals interacting trough binary games. We deduce a mathematical model of kinetic type and we prove some of its main properties, in particular the existence and the uniqueness of the solution, as well as on the asymptotic limit for quasi-invariant games. Finally, we will discuss some numerical simulations.

José A. Scheinkman (Princeton University)

Supply and Shorting in Speculative Markets

We study an equilibrium model of speculation in an asset with an exogenous supply when risk-neutral investors have fluctuating heterogeneous beliefs and face possibly different (quadratic) costs of holding long and short positions (joint work with Marcel Nutz).

Eitan Tadmor (University of Maryland and ETH-ITS)

Rules of Engagement, Emergence of Consensus and Hydrodynamic Flocking

We discuss several first- and second-order models encountered in opinion- and respectively flocking dynamics. The models are driven by different “rules of engagement”, which quantify how each member interacts with its immediate neighbors. We distinguish between local and global interactions and address the two related questions:
(i) how local rules of interaction lead, over time, to the emergence of consensus/flocking;
(ii) how the behavior of large crowds is captured by their hydrodynamic description.