V.Alfi F.Coccetti M.Marotta A.Petri L.Pietronero Exact results for the roughness of a finite size random walk Physica A: Statistical Mechanics and its Applications 370 127–131 2006 V.Alfi F.Coccetti M.Marotta L.Pietronero M.Takayasu Hidden forces and fluctuations from moving averages: A test study Physica A: Statistical Mechanics and its Applications 370 30–37 2006 V.Alfi A.DeMartino L.Pietronero A.Tedeschi Detecting the traders' strategies in minority-majority games and real stock-prices Physica A: Statistical Mechanics and its Applications 382 1–8 2007 V.Alfi G.Parisi L.Pietronero How people react to a deadline: the example of conference registration Nature Physics 11 xxx 2007 V.Alfi G.Parisi L.Pietronero How People React to a Deadline: The Distribution of Registrations of Statphys 23 2007 In Fig. 1 we show the number of registrations to Statphys 23 (full dots). Each point corresponds to one day and the deadline $T^*$=March 31 was the one corresponding to the early registration and abstract submission. We also plot the data corresponding to the a different conference (EP2DS 17) for which we have rescaled the total number of registration at its own $T^*$. The data of the two conferences are remarkably similar and are characterized by an initial linear behavior followed by a strong increase near $T^*$. This strong similarity suggests for a simple mechanism to describe the response of the people to a deadline and we propose have a simple model. The basic idea is that the pressure you have to register is proportional to the inverse of the remaining time to the deadline. This gives a probability, $p(t)$, to register at time $t$ that is $p(t) 1(T^*-t)$. From this the number of the registrations at time $t$ is: $$ N(t)=C_0^T^*p(t);dt=A(N_tot);(^*T^*-t). $$ The logarithmic singularity at the end is regularized by discretizing the integral with an interval of one day and the constant $A(N_tot)$ is fixed by the total number of final registration $N_tot$. As one can see in Fig. 1 this simple model fits the observed behavior extremely well. This permits to predict the total number of registrations already from the initial slope. A result that could have some practical interest. The model only assumes that the probability to register is the same for the whole interval of the the remaining time. In this respect there is no real tendency to shift the registration towards the deadline. The increase of pressure is just due to the approaching of the deadline. This situation may appear curious because one could have expected a stronger pressure to postpone the payment towards the deadline. In this respect, however, one should notice that the data in Fig. 1 refer only to the registration and not to the payment of the fee which could have been done also at a late time. V.Alfi G.Parisi L.Pietronero How People React to a Deadline: The Distribution of Registrations of Statphys 23 2007 In Fig. 1 we show the number of registrations to Statphys 23 (full dots). Each point corresponds to one day and the deadline $T^*$=March 31 was the one corresponding to the early registration and abstract submission. We also plot the data corresponding to the a different conference (EP2DS 17) for which we have rescaled the total number of registration at its own $T^*$. The data of the two conferences are remarkably similar and are characterized by an initial linear behavior followed by a strong increase near $T^*$. This strong similarity suggests for a simple mechanism to describe the response of the people to a deadline and we propose have a simple model. The basic idea is that the pressure you have to register is proportional to the inverse of the remaining time to the deadline. This gives a probability, $p(t)$, to register at time $t$ that is $p(t) 1(T^*-t)$. From this the number of the registrations at time $t$ is: $$ N(t)=C_0^T^*p(t);dt=A(N_tot);(^*T^*-t). $$ The logarithmic singularity at the end is regularized by discretizing the integral with an interval of one day and the constant $A(N_tot)$ is fixed by the total number of final registration $N_tot$. As one can see in Fig. 1 this simple model fits the observed behavior extremely well. This permits to predict the total number of registrations already from the initial slope. A result that could have some practical interest. The model only assumes that the probability to register is the same for the whole interval of the the remaining time. In this respect there is no real tendency to shift the registration towards the deadline. The increase of pressure is just due to the approaching of the deadline. This situation may appear curious because one could have expected a stronger pressure to postpone the payment towards the deadline. In this respect, however, one should notice that the data in Fig. 1 refer only to the registration and not to the payment of the fee which could have been done also at a late time. V.Alfi G.Parisi L.Pietronero How People React to a Deadline: The Distribution of Registrations of Statphys 23 2007 In Fig. 1 we show the number of registrations to Statphys 23 (full dots). Each point corresponds to one day and the deadline $T^*$=March 31 was the one corresponding to the early registration and abstract submission. We also plot the data corresponding to the a different conference (EP2DS 17) for which we have rescaled the total number of registration at its own $T^*$. The data of the two conferences are remarkably similar and are characterized by an initial linear behavior followed by a strong increase near $T^*$. This strong similarity suggests for a simple mechanism to describe the response of the people to a deadline and we propose have a simple model. The basic idea is that the pressure you have to register is proportional to the inverse of the remaining time to the deadline. This gives a probability, $p(t)$, to register at time $t$ that is $p(t) 1(T^*-t)$. From this the number of the registrations at time $t$ is: $$ N(t)=C_0^T^*p(t);dt=A(N_tot);(^*T^*-t). $$ The logarithmic singularity at the end is regularized by discretizing the integral with an interval of one day and the constant $A(N_tot)$ is fixed by the total number of final registration $N_tot$. As one can see in Fig. 1 this simple model fits the observed behavior extremely well. This permits to predict the total number of registrations already from the initial slope. A result that could have some practical interest. The model only assumes that the probability to register is the same for the whole interval of the the remaining time. In this respect there is no real tendency to shift the registration towards the deadline. The increase of pressure is just due to the approaching of the deadline. This situation may appear curious because one could have expected a stronger pressure to postpone the payment towards the deadline. In this respect, however, one should notice that the data in Fig. 1 refer only to the registration and not to the payment of the fee which could have been done also at a late time. V.Alfi A.Petri L.Pietronero A sum rule approach to detect complex correlation in time series 2007 A basic problem in the analysis of time series consists in unveiling and characterizing correlations among the variables at different times. In practice inmost cases this consists in considering the two point correlations over a long time series. Often complex properties are related to the long time behavior of these correlations. However, in many systems, like for example financial time series, simple correlations are intrinsically excluded by the arbitrage hypothesis. This leaves space for subtle complex correlations which are clearly difficult to detect. The usual approach is to focus on the pair correlations for grouped variables like in the problem of volatility clustering. Also in this case the availability of long time series is fundamental. This poses another problem because the stationarity hypothesis is not always appropriate. Inspired by these problems we introduce a new method to detect complex correlations in time series of finite size. The method comes from the Spitzerกวs identity which controls the extremal values for sums of random variables. The basic idea is that a deviation from this identity is a sign of correlations in the variables and it corresponds to a sort of sum rule for correlations of any extension also in non stationary processes. We have tested the method which has only four point correlations. The application to real financial data shows that the method is a practical tool to detect correlations of any type even in finite time series. This is usually not possible with the standard statistical tools.