arXiv subjects

Pierre Gosselin

Publications and source records attributed to Pierre Gosselin.

At least 19 recordsLinked to original sources

arXiv2025

Financial Interactions and Collective States: Part II. Banks, Investors and Firms

In a previous paper, we applied a field formalism to analyze capital allocation and accumulation within a microeconomic framework of investors and firms. The financial connections were modeled by a field of stakes, representing the links between agents. We showed that the resulting collective states were composed of interconnected groups of agents defined by their connections, their returns and disposable capital. However, within this framework, the collective states exhibited structural instability, as capital shortages in specific sectors could trigger cascades of defaults. The present model refines this framework by introducing a third type of agent, banks, a type of investor that can create money through loans. We show that money creation neither eliminates systemic instability nor prevents the emergence of defaults. In fact, the effect of banks on system stability and defaults is ambiguous: When banks favor firms over investors, money creation stabilizes the system by providing the necessary capital to prevent initial defaults, whereas when banks favor investors over firms, investors' influence is strengthened, potentially amplifying instability and defaults. Moreover, regardless of whether they favor investors or firms, banks may facilitate the propagation of defaults once they have started. Ultimately, because banks are themselves investors, the emergence of highly capitalized, high-return banks can directly generate instability in the system. Beyond these mechanisms, the analysis reveals the structural limits of macroprudential regulation. Highly capitalized, high-return investors and banks may appear more diversified and resilient, yet they constitute the primary source of endogenous instability. The model thus highlights that systemic fragility is inherent to the very structure of financial interdependence and capital flows.

cond-mat.other↗

arXiv2025

Financial Interactions and Collective States: Part I. Investors and Firms

In a previous work, we applied a field formalism to analyze capital allocation and accumulation within a network of investors and firms. In that framework, financial agents could invest in firms or in other investors, and banks-introduced as investors with a credit multiplier-could play a stabilizing or destabilizing role. Collective states emerged from these interactions, reflecting different configurations of capital distribution and stability in the financial system. However, these results relied on the assumption that financial connections were exogenous. The present paper removes this assumption by modeling financial connections as dynamic endogenous variables. Specifically, we extend the framework by introducing a field representation of the network of financial connections. The collective states previously identified are now embedded in a broader class of states, characterized by the structure of investment stakes among investors. We show that these collective states consist of inter-connected groups of agents, along with their returns and disposable capital. The model reveals the emergence of two investor classes: high- and low-return (and capital) agents. High-return investors, in particular, act as a source of instability within the system, enabling transitions between configurations. In each collective state, some sectors may experience defaults. When the collective state exhibits specific structural conditions, defaults may spread across a significant share of the group.

cond-mat.other↗

arXiv2025

Statistical Field Theory and Neural Structures Dynamics V: Synthesis and extensions

We present a unified field-theoretic framework for the dynamics of activity and connectivity in interacting neuronal systems. Building upon previous works, where a field approach to activity--connectivity dynamics, formation of collective states and effective fields of collective states were successively introduced, the present paper synthesizes and extends these results toward a general description of multiple hierarchical collective structures. Starting with the dynamical system representing collective states in terms of connections, activity levels, and internal frequencies, we analyze its stability, emphasizing the possibility of transitions between configurations. Then, turning to the field formalism of collective states, we extend this framework to include substructures (subobjects) participating in larger assemblies while retaining intrinsic properties. We define activation classes describing compatible or independent activity patterns between objects and subobjects, and study stability conditions arising from their alignment or mismatch. The global system is described as the collection of landscapes of coexisting and interacting collective states, each characterized both by continuous (activity, frequency) and discrete (class) variables. A corresponding field formalism is developed, with an action functional incorporating both internal dynamics and interaction terms. This nonlinear field model captures cascading transitions between collective states and the formation of composite structures, providing a coherent theoretical basis for emergent neuronal assemblies and their mutual couplings.

q-bio.NC↗

arXiv2025

Decomposition of State Spaces into Subobjects in Quantum Field Theory

This paper introduces a comprehensive formalism for decomposing the state space of a quantum field into several entangled subobjects, i.e., fields generating a subspace of states. Projecting some of the subobjects onto degenerate background states reduces the system to an effective field theory depending on parameters representing the degeneracies. Notably, these parameters are not exogenous. The entanglement among subobjects in the initial system manifests as an interrelation between parameters and non-projected subobjects. Untangling this dependency necessitates imposing linear first-order equations on the effective field. The geometric characteristics of the parameter spaces depend on both the effective field and the background of the projected subobjects. The system, governed by arbitrary variables, has no dynamics, but the projection of some subobjects can be interpreted as slicing the original state space according to the lowest eigenvalues of a parameter-dependent family of operators. The slices can be endowed with amplitudes similar to some transitions between each other, contingent upon these eigenvalues. Averaging over all possible transitions shows that the amplitudes are higher for maps with increased eigenvalue than for maps with decreasing eigenvalue.

quant-ph↗

arXiv2024

Statistical Field Theory and Neural Structures Dynamics IV: Field-Theoretic Formalism for Interacting Collective States

Building upon the findings presented in the first three papers of this series, we formulate an effective field theory for interacting collective states. These states consist of a large number of interconnected neurons and are distinguished by their intrinsic activity. The field theory encompasses an infinite set of fields, each of which characterizes the dynamics of a specific type of collective state. Interaction terms within the theory drive transitions between various collective states, allowing us to describe processes such as activation, association, and deactivation of these states.

physics.bio-ph↗

arXiv2024

Financial Interactions and Capital Accumulation

In a series of precedent papers, we have presented a comprehensive methodology, termed Field Economics, for translating a standard economic model into a statistical field-formalism framework. This formalism requires a large number of heterogeneous agents, possibly of different types. It reveals the emergence of collective states among these agents or type of agents while preserving the interactions and microeconomic features of the system at the individual level. In two prior papers, we applied this formalism to analyze the dynamics of capital allocation and accumulation in a simple microeconomic framework of investors and firms.Building upon our prior work, the present paper refines the initial model by expanding its scope. Instead of considering financial firms investing solely in real sectors, we now suppose that financial agents may also invest in other financial firms. We also introduce banks in the system that act as investors with a credit multiplier. Two types of interaction are now considered within the financial sector: financial agents can lend capital to, or choose to buy shares of, other financial firms. Capital now flows between financial agents and is only partly invested in real sectors, depending on their relative returns. We translate this framework into our formalism and study the diffusion of capital and possible defaults in the system, both at the macro and micro level.At the macro level, we find that several collective states may emerge, each characterized by a distinct level of average capital and investors per sector. These collective states depend on external parameters such as level of connections between investors or firms' productivity.The multiplicity of possible collective states is the consequence of the nature of the system composed of interconnected heterogeneous agents. Several equivalent patterns of returns and portfolio allocation may emerge. The multiple collective states induce the unstable nature of financial markets, and some of them include defaults may emerge. At the micro level, we study the propagation of returns and defaults within a given collective state. Our findings highlight the significant role of banks, which can either stabilize the system through lending activities or propagate instability through loans to investors.

physics.soc-ph↗

arXiv2023

A Statistical Field Perspective on Capital Allocation and Accumulation

This paper provides a general method to translate a standard economic model with a large number of agents into a field-formalism model. This formalism preserves the system's interactions and microeconomic features at the individual level but reveals the emergence of collective states.We apply this method to a simple microeconomic framework of investors and firms. Both macro and micro aspects of the formalism are studied.At the macro-scale, the field formalism shows that, in each sector, three patterns of capital accumulation may emerge. A distribution of patterns across sectors constitute a collective state. Any change in external parameters or expectations in one sector will affect neighbouring sectors, inducing transitions between collective states and generating permanent fluctuations in patterns and flows of capital. Although changes in expectations can cause abrupt changes in collective states, transitions may be slow to occur. Due to its relative inertia, the real economy is bound to be more affected by these constant variations than the financial markets.At the micro-scale we compute the transition functions of individual agents and study their probabilistic dynamics in a given collective state, as a function of their initial state. We show that capital accumulation of an individual agent depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Moreover, the firm must also consider the effects of competition in the intermediate sectors that tends to oust under-capitalized firm towards sectors with lower average capital. For investors, capital allocation depends on their short and long-term returns and investors will tend to reallocate their capital to maximize both. The higher their level of capital, the stronger the re-allocation will be.

q-fin.GN↗

arXiv2023

Statistical Field Theory and Neural Structures Dynamics I: Action Functionals, Background States and External Perturbations

This series of papers models the dynamics of a large set of interacting neurons within the framework of statistical field theory. The system is described using a two-field model. The first field represents the neuronal activity, while the second field accounts for the interconnections between cells. This model is derived by translating a probabilistic model involving a large number of interacting cells into a field formalism. The current paper focuses on deriving the background fields of the system, which describe the potential equilibria in terms of interconnected groups. Dynamically, we explore the perturbation of these background fields, leading to processes such as activation, association, and reactivation of groups of cells.

physics.bio-ph↗

arXiv2023

Statistical Field Theory and Neural Structures Dynamics II: Signals Propagation, Interferences, Bound States

We continue our study of a field formalism for large sets of interacting neurons, together with their connectivity functions. Expanding upon the foundation laid in ([9]), we formulate an effective formalism for the connectivity field in the presence of external sources. We proceed to deduce the propagation of external signals within the system. This enables us to investigate the activation and association of groups of bound cells.

physics.bio-ph↗

arXiv2023

Statistical Field Theory and Neural Structures Dynamics III: Effective Action for Connectivities, Interactions and Emerging Collective States

This paper elaborates on the effective field theory for the connectivity field previously introduced in ([7]). We demonstrate that dynamic interactions among connectivities induce modifications in the background state. These modifications can be understood as the emergence of interacting collective states above the background state. The emergence of such states is contingent on both interactions and the shape of the static or quasi-static background, which acts as a conditioning factor for potential emerging states.

physics.bio-ph↗

arXiv2023

A Statistical Field Perspective on Capital Allocation and Accumulation: Individual dynamics

We have shown, in a series of articles, that a classical description of a large number of economic agents can be replaced by a statistical fields formalism. To better understand the accumulation and allocation of capital among different sectors, the present paper applies this statistical fields description to a large number of heterogeneous agents divided into two groups. The first group is composed of a large number of firms in different sectors that collectively own the entire physical capital. The second group, investors, holds the entire financial capital and allocates it between firms across sectors according to investment preferences, expected returns, and stock prices variations on financial markets. In return, firms pay dividends to their investors. Financial capital is thus a function of dividends and stock valuations, whereas physical capital is a function of the total capital allocated by the financial sector. Whereas our previous work focused on the background fields that describe potential long-term equilibria, here we compute the transition functions of individual agents and study their probabilistic dynamics in the background field, as a function of their initial state. We show that capital accumulation depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Thus, an under-capitalized firm reaching a high-capital sector will experience a loss of attractiveness, and subsequently, in investors. Moreover, the firm must also consider the effects of competition in the intermediate sectors. An under-capitalized firm will tend to be ousted out towards sectors with lower average capital, while an over-capitalized firm will tend to shift towards higher averagecapital sectors. For investors, capital allocation depends on their short and long-term returns. These returns are not independent: in the short-term, returns are composed of both the firm's dividends and the increase in its stock prices. In the long-term, returns are based on the firm's growth expectations, but also, indirectly, on expectations of higher stock prices. Investors' capital allocation directly depends on the volatility of stock prices and {\ldots}rms'dividends. Investors will tend to reallocate their capital to maximize their short and long-term returns. The higher their level of capital, the stronger the reallocation will be.

q-fin.GN↗

arXiv2022

Statistical Field Theory and Networks of Spiking Neurons

This paper models the dynamics of a large set of interacting neurons within the framework of statistical field theory. We use a method initially developed in the context of statistical field theory [44] and later adapted to complex systems in interaction [45][46]. Our model keeps track of individual interacting neurons dynamics but also preserves some of the features and goals of neural field dynamics, such as indexing a large number of neurons by a space variable. Thus, this paper bridges the scale of individual interacting neurons and the macro-scale modelling of neural field theory.

q-bio.NC↗

arXiv2022

Financial Markets and the Real Economy: A Statistical Field Perspective on Capital Allocation and Accumulation

This paper provides a general method to directly translate a classical economic framework with a large number of agents into a field-formalism model. This type of formalism allows the analytical treatment of economic models with an arbitrary number of agents, while preserving the system's interactions and microeconomic features of the individual level.We apply this methodology to model the interactions between financial markets and the real economy, described in a classical framework of a large number of heterogeneous agents, investors and firms. Firms are spread among sectors but may shift between sectors to improve their returns. They compete by producing differentiated goods and reward their investors by paying dividends and through their stocks' valuation. Investors invest in firms and move along sectors based on firms' expected long-run returns.The field-formalism model derived from this framework allows for collective states to emerge. We show that the number of firms in each sector depends on the aggregate financial capital invested in the sector and its firms' expected long-term returns. Capital accumulation in each sector depends both on short-term returns and expected long-term returns relative to neighbouring sectors.For each sector, three patterns of accumulation emerge. In the first pattern, the dividend component of short-term returns is determinant for sectors with small number of firms and low capital. In the second pattern, both short and long-term returns in the sector drive intermediate-to-high capital. In the third pattern, higher expectations of long-term returns drive massive inputs of capital.Instability in capital accumulation may arise among and within sectors. We therefore widen our approach and study the dynamics of the collective configurations, in particular interactions between average capital and expected long-term returns, and show that overall stability crucially depends on the expectations' formation process.Expectations that are highly reactive to capital variations stabilize high capital configurations, and drive low-to-moderate capital sectors towards zero or a higher level of capital, depending on their initial capital. Inversely, low-to moderate capital configurations are stabilized by expectations moderately reactive to capital variations, and drive high capital sectors towards more moderate level of capital equilibria.Eventually, the combination of expectations both highly sensitive to exogenous conditions and highly reactive to variations in capital imply that large fluctuations of capital in the system, at the possible expense of the real economy.

q-fin.GN↗

arXiv2021

Machine learning approaches to identify thresholds in a heat-health warning system context

During the last two decades, a number of countries or cities established heat-health warning systems in order to alert public health authorities when some heat indicator exceeds a predetermined threshold. Different methods were considered to establish thresholds all over the world, each with its own strengths and weaknesses. The common ground is that current methods are based on exposure-response function estimates that can fail in many situations. The present paper aims at proposing several data-driven methods to establish thresholds using historical data of health issues and environmental indicators. The proposed methods are model-based regression trees (MOB), multivariate adaptive regression splines (MARS), the patient rule-induction method (PRIM) and adaptive index models (AIM). These methods focus on finding relevant splits in the association between indicators and the health outcome but do it in different fashions. A simulation study and a real-world case study hereby compare the discussed methods. Results show that proposed methods are better at predicting adverse days than current thresholds and benchmark methods. The results nonetheless suggest that PRIM is overall the more reliable method with low variability of results according to the scenario or case.

stat.ME↗

arXiv2019

A Statistical Field Approach to Capital Accumulation

This paper presents a model of capital accumulation for a large number of heterogenous producer-consumers in an exchange space in which interactions depend on agents' positions. Each agent is described by his production, consumption, stock of capital, as well as the position he occupies in this abstract space. Each agent produces one differentiated good whose price is fixed by market clearing conditions. Production functions are Cobb-Douglas, and capital stocks follow the standard capital accumulation dynamic equation. Agents consume all goods but have a preference for goods produced by their closest neighbors. Agents in the exchange space are subject both to attractive and repulsive forces. Exchanges drive agents closer, but beyond a certain level of proximity, agents will tend to crowd out more distant agents. The present model uses a formalism based on statistical field theory developed earlier by the authors. This approach allows the analytical treatment of economic models with an arbitrary number of agents, while preserving the system's interactions and complexity at the individual level. Our results show that the dynamics of capital accumulation and agents' position in the exchange space are correlated. Interactions in the exchange space induce several phases of the system. A first phase appears when attractive forces are limited. In this phase, an initial central position in the exchange space favors capital accumulation in average and leads to a higher level of capital, while agents far from the center will experience a slower accumulation process. A high level of initial capital drives agents towards a central position, i.e. improve the terms of their exchanges: they experience a higher demand and higher prices for their product. As usual, high capital productivity favors capital accumulation, while higher rates of capital depreciation reduce capital stock. In a second phase, attractive forces are predominant. The previous results remain, but an additional threshold effect appears. Even though no restriction was imposed initially on the system, two types of agents emerge, depending on their initial stock of capital. One type of agents will remain above the capital threshold and occupy and benefit from a central position. The other type will remain below the threshold, will not be able to break it and will remain at the periphery of the exchange space. In this phase, capital distribution is less homogenous than in the first phase.

q-fin.GN↗

arXiv2018

A new look at weather-related health impacts through functional regression

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

stat.AP↗

arXiv2018

A Path Integral Approach to Business Cycle Models with Large Number of Agents

This paper presents an analytical treatment of economic systems with an arbitrary number of agents that keeps track of the systems' interactions and agents' complexity. This formalism does not seek to aggregate agents. It rather replaces the standard optimization approach by a probabilistic description of both the entire system and agents'behaviors. This is done in two distinct steps. A first step considers an interacting system involving an arbitrary number of agents, where each agent's utility function is subject to unpredictable shocks. In such a setting, individual optimization problems need not be resolved. Each agent is described by a time-dependent probability distribution centered around his utility optimum. The entire system of agents is thus defined by a composite probability depending on time, agents' interactions and forward-looking behaviors. This dynamic system is described by a path integral formalism in an abstract space-the space of the agents' actions-and is very similar to a statistical physics or quantum mechanics system. We show that this description, applied to the space of agents'actions, reduces to the usual optimization results in simple cases. Compared to a standard optimization, such a description markedly eases the treatment of systems with small number of agents. It becomes however useless for a large number of agents. In a second step therefore, we show that for a large number of agents, the previous description is equivalent to a more compact description in terms of field theory. This yields an analytical though approximate treatment of the system. This field theory does not model the aggregation of a microeconomic system in the usual sense. It rather describes an environment of a large number of interacting agents. From this description, various phases or equilibria may be retrieved, along with individual agents' behaviors and their interactions with the environment. For illustrative purposes, this paper studies a Business Cycle model with a large number of agents.

econ.GN↗

arXiv2018

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

stat.AP↗