This PhD thesis aims at investigating the cognitive and behavioral processes involved in collective decision-making in pedestrian groups with an approach that tightly combines experiments with models. We are looking for a student, familiar with the tools of statistical physics and the use of numerical simulations, which will participate in the design and analysis of dedicated experiments, and in the analytical and numerical study of theoretical models inspired from statistical physics and based on these experiments. Only non-French students can apply to the present PhD fellowship. Candidates must contact (complete CV and, if possible, a recommendation letter from a responsible of Master studies) both advisors (see the last page of this document) ideally before June 16th 2013. The position will start on October 2013 and the net salary (for 36 months) is around 1450€/month, including medical coverage. LPT and CRCA will cover travel expanses to Toulouse, a lively student city in the southwest of France. [more]

We study a phenomenological model for the continuous double auction, equivalent to two independent $M/M/1$ queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior of prices and logarithmic returns are observed. In the ergodic regime, prices are unstable and one can observe an intermittent behavior in the logarithmic returns. On the contrary, non-ergodicity triggers stability of prices, even if two different regimes can be seen. [more]

We have analyzed the Indices of Industrial Production (Seasonal Adjustment Index) for a long period of 240 months (January 1988 to December 2007) to develop a deeper understanding of the economic shocks. The angular frequencies estimated using the Hilbert transformation, are almost identical for the 16 industrial sectors. Moreover, the partial phase locking was observed for the 16 sectors. These are the direct evidence of the synchronization in the Japanese business cycle. We also showed that the information of the economic shock is carried by the phase time-series. The common shock and individual shocks are separated using phase time-series. The former dominates the economic shock in all of 1992, 1998 and 2001. The obtained results suggest that the business cycle may be described as a dynamics of the coupled limit-cycle oscillators exposed to the common shocks and random individual shocks. [more]

We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous auto-regressive component and a random rescaling factor embodying exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance in terms of obtaining closed formulas for derivative pricing. Further important features are: The possibility of making contact, in certain limits, with auto-regressive models widely used in finance; The possibility of partially resolving the endogenous and exogenous components of the volatility, with consistent results when applied to historical series. [more]

This paper sets up a methodology for approximately solving optimal investment problems using duality methods combined with Monte Carlo simulations. In particular, we show how to tackle high dimensional problems in incomplete markets, where traditional methods fail due to the curse of dimensionality. [more]

One of the most important features of spatial networks such as transportation networks, power grids, Internet, neural networks, is the existence of a cost associated with the length of links. Such a cost has a profound influence on the global structure of these networks which usually display a hierarchical spatial organization. The link between local constraints and large-scale structure is however not elucidated and we introduce here a generic model for the growth of spatial networks based on the general concept of cost benefit analysis. This model depends essentially on one single scale and produces a family of networks which range from the star-graph to the minimum spanning tree and which are characterised by a continuously varying exponent. We show that spatial hierarchy emerges naturally, with structures composed of various hubs controlling geographically separated service areas, and appears as a large-scale consequence of local cost-benefit considerations. Our model thus provides the first building blocks for a better understanding of the evolution of spatial networks and their properties. We also find that, surprisingly, the average detour is minimal in the intermediate regime, as a result of a large diversity in link lengths. Finally, we estimate the important parameters for various world railway networks and find that --remarkably-- they all fall in this intermediate regime, suggesting that spatial hierarchy is a crucial feature for these systems and probably possesses an important evolutionary advantage. [more]

We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon sessions. After calculating the realized volatilities at various sampling frequencies we evaluate the bias from the microstructure noise as a function of sampling frequency. Taking into account of the bias to realized volatility we examine returns standardized by realized volatilities and confirm that price returns on the Tokyo Stock Exchange are described approximately by Gaussian time series with time-varying volatility, i.e. consistent with a mixture of distributions hypothesis. [more]

The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model. [more]

Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition and the methods used to understand them analytically. We emphasize the power of concepts and tools from statistical mechanics to understand and explain fully collective phenomena such as phase transitions and long memory, and the mapping between agent heterogeneity and physical disorder. As these methods can be applied to any large-scale model made up of heterogeneous adaptive agent with non-linear interaction, they provide a prospective unifying paradigm for many scientific disciplines. [more]

The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) framework. Behavioural patterns are injected into an asset pricing framework through the so-called `Break Point Date', which allows us to examine their direct impact. In particular, we analyse the dynamics of the model around the behavioural break. Price behaviour of 30 Dow Jones Industrial Average constituents covering five particularly turbulent U.S. stock market periods reveals interesting pattern in this aspect. To replicate it, we apply numerical analysis using the Heterogeneous Agent Model extended with the selected findings from behavioural finance: herding, overconfidence, and market sentiment. We show that these behavioural breaks can be well modelled via the Heterogeneous Agent Model framework and they extend the original model considerably. Various modifications lead to significantly different results and model with behavioural breaks is also able to partially replicate price behaviour found in the data during turbulent stock market periods. [more]

We investigate the relation between economic growth and equality in a modified version of the agent-based asset exchange model (AEM). The modified model is a driven system that for a range of parameter space is effectively ergodic in the limit of an infinite system. We find that the belief that "a rising tide lifts all boats" does not always apply, but the effect of growth on the wealth distribution depends on the nature of the growth. In particular, we find that the rate of growth, the way the growth is distributed, and the percentage of wealth exchange determine the degree of equality. We find strong numerical evidence that there is a phase transition in the modified model, and for a part of parameter space the modified AEM acts like a geometric random walk. [more]

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population. [more]

We study a subset of the movie collaboration network, imdb.com, where only adult movies are included. We show that there are many benefits in using such a network, which can serve as a prototype for studying social interactions. We find that the strength of links, i.e., how many times two actors have collaborated with each other, is an important factor that can significantly influence the network topology. We see that when we link all actors in the same movie with each other, the network becomes small-world, lacking a proper modular structure. On the other hand, by imposing a threshold on the minimum number of links two actors should have to be in our studied subset, the network topology becomes naturally fractal. This occurs due to a large number of meaningless links, namely, links connecting actors that did not actually interact. We focus our analysis on the fractal and modular properties of this resulting network, and show that the renormalization group analysis can characterize the self-similar structure of these networks. [more]

The focus of this work is on developing probabilistic models for user activity in social networks by incorporating the social network influence as perceived by the user. For this, we propose a coupled Hidden Markov Model, where each user's activity evolves according to a Markov chain with a hidden state that is influenced by the collective activity of the friends of the user. We develop generalized Baum-Welch and Viterbi algorithms for model parameter learning and state estimation for the proposed framework. We then validate the proposed model using a significant corpus of user activity on Twitter. Our numerical studies show that with sufficient observations to ensure accurate model learning, the proposed framework explains the observed data better than either a renewal process-based model or a conventional uncoupled Hidden Markov Model. We also demonstrate the utility of the proposed approach in predicting the time to the next tweet. Finally, clustering in the model parameter space is shown to result in distinct natural clusters of users characterized by the interaction dynamic between a user and his network. [more]

The increasing interdependencies between the world’s technological, socio-economic, and environmental systems have the potential to create global catastrophic risks. We may have to re-design many global networks, otherwise they could turn into "global time bombs". [more]

In this paper we argue that if we want to find a more satisfactory approach to tackling the major socio-economic problems we are facing, we need to thoroughly rethink the basic assumptions of macroeconomics and financial theory. Making minor modifications to the standard models to remove "imperfections" is not enough, the whole framework needs to be revisited. [more]

The International Conference on EconoPhysics which will be held in Kavala Greece on September, 13-14, 2013. The conference is organized by Kavala’s Institute of Technology. Research papers have to be submitted by June 30th, 2013. [more]

Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "Jump" events, corresponding to sudden changes of the best limit price, must also be included as birth-death terms in the Fokker-Planck equation. All quantities involved in the equation can be calibrated using high-frequency data on best quotes. One of our central finding is the the dynamical process is approximately scale invariant, i.e., the only relevant variable is the ratio of the current volume in the queue to its average value. While the latter shows intraday seasonalities and strong variability across stocks and time periods, the dynamics of the rescaled volumes is universal. In terms of rescaled volumes, we found that the drift has a complex two-dimensional structure, which is a sum of a gradient contribution and a rotational contribution, both stable across stocks and time. This drift term is entirely responsible for the dynamical correlations between the ask queue and the bid queue. [more]

This paper investigates the relevance of the No-Ponzi game condition for public debt (i.e. the public debt growth rate has to be lower than the real interest rate, a necessary assumption for Ricardian equivalence) and of the transversality condition for the GDP growth rate (i.e. the GDP growth rate has to be lower than the real interest rate). First, on the unbalanced panel of 21 countries from 1961 to 2010 available in OECD database, those two conditions were simultaneously validated only for 29% of the cases under examination. Second, those two conditions were more frequent in the 1980s and the 1990s when monetary policies were more restrictive. Third, in tune with the Keynesian view, when the real interest rate is higher than the GDP growth, it corresponds to 75% of the cases of the increases of the debt/GDP ratio but to only 43% of the cases of the decreases of the debt/GDP ratio (fiscal consolidations). [more]