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We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. We firstly propose how compared to normal densities, our target time series seem to be associated with a higher number of rare events, and Gaussian statistics tend to underestimate these events' frequency drastically. To this end, we suggest that by imposing Gaussian densities to the natural processes, one will seriously neglect the existence of extreme events in many circumstances. The central question of our study concerns the consideration of the effects of these rare events in the corresponding probability densities and studying their role from the point of view of quantum measurements. To model the non-Gaussian behavior of these time-series, we utilize the multifractal random walk (MRW) approach and control the non-Gaussianity parameter $\lambda$ accordingly. Using the framework of quantum mechanics, we then examine the role of $\lambda$ in quantum potentials derived for these time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for the rare events. We thus conclude that these events could generate a potential barrier that the system, lingering in a non-Gaussian high-frequency region, encounters, and their role becomes more prominent when it comes to transversing this barrier. In this study, as an example of the application of quantum potential outside of the micro-world, we compute the quantum potentials for the S\&P financial market time series to verify the presence of rare events in the non-Gaussian densities for this real data and remark the deviation from the Gaussian case., Comment: 7 pages, 2 figures

Since 2008, the network analysis of financial systems is one of the most important subjects in economics. In this paper, we have used the complexity approach and Random Matrix Theory (RMT) for analyzing the global banking network. By applying this method on a cross border lending network, it is shown that the network has been denser and the connectivity between peripheral nodes and the central section has risen. Also, by considering the collective behavior of the system and comparing it with the shuffled one, we can see that this network obtains a specific structure. By using the inverse participation ratio concept, we can see that after 2000, the participation of different modes to the network has increased and tends to the market mode of the system. Although no important change in the total market share of trading occurs, through the passage of time, the contribution of some countries in the network structure has increased. The technique proposed in the paper can be useful for analyzing different types of interaction networks between countries., Comment: 5 pages, 4 figures

In order to extract hidden joint information from two possibly uncorrelated time-series, we explored the measures of network science. Alongside common methods in time-series analysis of the economic markets, mapping the joint structure of two time-series onto a network provides insight into hidden aspects embedded in the couplings. We discretize the amplitude of two time-series and investigate relative simultaneous locations of those amplitudes. Each segment of a discretized amplitude is considered as a node. The simultaneity of the amplitudes of the two time-series is considered as the edges in the network. The frequency of occurrences forms the weighted edges. In order to extract information, we need to measure that to what extent the coupling deviates from the coupling of two uncoupled series. Also, we need to measure that to what extent the couplings inherit their characteristics from a Gaussian distribution or a non-Gaussian distribution. We mapped the network from two surrogate time-series. The results show that the couplings of markets possess some features which diverge from the same features of the network mapped from white noise, and from the network mapped from two surrogate time-series. These deviations prove that there exist joint information and cross-correlation therein. By applying the network's topological and statistical measures and the deformation ratio in the joint probability distribution, we distinguished basic structures of cross-correlation and coupling of cross-markets. It was discovered that even two possibly known uncorrelated markets may possess some joint patterns with each other. Thereby, those markets should be examined as coupled and \textit{weakly} coupled markets., Comment: 7 pages, 4 figures

Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To explore the influence of investment size (trading volume), price-wise (gain/loss), and time-scale effects, we analyzed the price and trading volume and their coupling by applying the MF-DXA method. Our results imply that price, trading volume, and price-volume coupling exhibit a power law and are also multifractal. Meanwhile, considering developed markets, the price-volume couplings are significantly negatively correlated. However, in emerging markets, the price has less of a contribution to price-volume coupling. In emerging markets in comparison with the developed markets, trading volume and price are more independent., Comment: 9 pages, 5 figures

In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The dataset is heterogeneous and consists of a mixture of computer generated automatic processes and manual human tasks. The goal is to work out a decision model, which represents the underlying tasks that make up the loan application service, and to assess potential areas of improvement of the institution's internal processes. To this end we study the impact of incomplete event logs for the extraction and analysis of business processes. It is quite common that event logs are incomplete with several amounts of missing information (for instance, workers forget to register their tasks). Absence of data is translated into a drastic decrease of precision and compromises the decision models, leading to biased and unrepresentative results. We investigate how classical probabilistic models are affected by incomplete event logs and we explore quantum-like probabilistic inferences as an alternative mathematical model to classical probability. This work represents a first step towards systematic investigation of the impact of quantum interference in a real life large scale decision scenario. The results obtained in this study indicate that, under high levels of uncertainty, the quantum-like models generate quantum interference terms, which allow an additional non-linear parameterisation of the data. Experimental results attest the efficiency of the quantum-like Bayesian networks, since the application of interference terms is able to reduce the error percentage of inferences performed over quantum-like models when compared to inferences produced by classical models., Comment: 15 images, 33 pages

Expected utility theory (EUT) is widely used in economic theory. However, its subjective probability formulation, first elaborated by Savage, is linked to Ellsberg-like paradoxes and ambiguity aversion. This has led various scholars to work out non-Bayesian extensions of EUT which cope with its paradoxes and incorporate attitudes toward ambiguity. A variant of the Ellsberg paradox, recently proposed by Mark Machina and confirmed experimentally, challenges existing non-Bayesian models of decision-making under uncertainty. Relying on a decade of research which has successfully applied the formalism of quantum theory to model cognitive entities and fallacies of human reasoning, we put forward a non-Bayesian extension of EUT in which subjective probabilities are represented by quantum probabilities, while the preference relation between acts depends on the state of the situation that is the object of the decision. We show that the benefits of using the quantum theoretical framework enables the modeling of the Ellsberg and Machina paradoxes, as the representation of ambiguity and behavioral attitudes toward it. The theoretical framework presented here is a first step toward the development of a `state-dependent non-Bayesian extension of EUT' and it has potential applications in economic modeling., Comment: 25 pages, standard latex. arXiv admin note: text overlap with arXiv:1510.09058

We discuss a non linear extension of a model of alliances in politics, recently proposed by one of us. The model is constructed in terms of operators, describing the \emph{interest} of three parties to form, or not, some political alliance with the other parties. The time evolution of what we call \emph{the decision functions} is deduced by introducing a suitable hamiltonian, which describes the main effects of the interactions of the parties amongst themselves and with their \emph{environments}, {which are }generated by their electors and by people who still have no clear {idea }for which party to vote (or even if to vote). The hamiltonian contains some non-linear effects, which takes into account the role of a party in the decision process of the other two parties. Moreover, we show how the same hamiltonian can also be used to construct a formal structure which can describe the dynamics of buying and selling financial assets (without however implying a specific price setting mechanism)., Comment: arXiv admin note: text overlap with arXiv:1502.01732

The applications of techniques from statistical (and classical) mechanics to model interesting problems in economics and finance has produced valuable results. The principal movement which has steered this research direction is known under the name of `econophysics'. In this paper, we illustrate and advance some of the findings that have been obtained by applying the mathematical formalism of quantum mechanics to model human decision making under `uncertainty' in behavioral economics and finance. Starting from Ellsberg's seminal article, decision making situations have been experimentally verified where the application of Kolmogorovian probability in the formulation of expected utility is problematic. Those probability measures which by necessity must situate themselves in Hilbert space (such as `quantum probability') enable a faithful representation of experimental data. We thus provide an explanation for the effectiveness of the mathematical framework of quantum mechanics in the modeling of human decision making. We want to be explicit though that we are not claiming that decision making has microscopic quantum mechanical features., Comment: 13 pages, standard Latex. arXiv admin note: text overlap with arXiv:1302.3850

This paper is devoted to the application of the mathematical formalism of quantum mechanics to social (political) science. By using the quantum dynamical equations we model the process of decision making in US elections. The crucial point we attempt to make is that the voter's mental state can be represented as a superposition of two possible choices for either republicans or democrats. However, reality dictates a more complicated situation: typically a voter participates in two elections, i.e. the congress and the presidential elections. In both elections he/she has to decide between two choices. This very feature of the US election system requires that the mental state is represented by a 2-qubit state corresponding to the superposition of 4 different choices (e.g. for republicans in the congress; for the president as a democrat). The main issue of this paper is to describe the dynamics of the voters' mental states taking in account the mental and socio- political environment. What is truly novel in this paper is that instead of using Schr\"odinger's equation to describe the dynamics in an absence of interactions, we here apply the quantum master equation. This equation describes quantum decoherence, i.e., resolution from superposition to a definite choice., Comment: 26 pages, 4 figures

We prove a theorem which shows that a collection of experimental data of probabilistic weights related to decisions with respect to situations and their disjunction cannot be modeled within a classical probabilistic weight structure in case the experimental data contain the effect referred to as the 'disjunction effect' in psychology. We identify different experimental situations in psychology, more specifically in concept theory and in decision theory, and in economics (namely situations where Savage's Sure-Thing Principle is violated) where the disjunction effect appears and we point out the common nature of the effect. We analyze how our theorem constitutes a no-go theorem for classical probabilistic weight structures for common experimental data when the disjunction effect is affecting the values of these data. We put forward a simple geometric criterion that reveals the non classicality of the considered probabilistic weights and we illustrate our geometrical criterion by means of experimentally measured membership weights of items with respect to pairs of concepts and their disjunctions. The violation of the classical probabilistic weight structure is very analogous to the violation of the well-known Bell inequalities studied in quantum mechanics. The no-go theorem we prove in the present article with respect to the collection of experimental data we consider has a status analogous to the well known no-go theorems for hidden variable theories in quantum mechanics with respect to experimental data obtained in quantum laboratories. For this reason our analysis puts forward a strong argument in favor of the validity of using a quantum formalism for modeling the considered psychological experimental data as considered in this paper., Comment: 15 pages, 4 figures

Probability interference is a fundamental characteristic of quantum mechanics. In this paper we attempt to show with the help of some examples, where this fundamental trait of quantum physics can be found back in a social science environment. In order to support our thesis that interference can possibly be found back in many other macro-scopic areas, we proceed in setting up an experimental test.