arXiv research

Model captures neural activity related to behavior while separating internal computations.

Paper models market dynamics using bull and bear forces.

The majority of real-world networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook, ...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics of behavioral roles representing the main connectivity patterns over time. In th…

Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a …

Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.

We derive a class of macroscopic differential equations that describe collective adaptation, starting from a discrete-time stochastic microscopic model. The behavior of each agent is a dynamic balance between adaptation that locally achieves the best action and memory loss that leads to randomized behavior. We show tha…

Empirical study shows carriers ignore past shippers' behavior, focusing only on current actions.

We derive behavioral finance option pricing formulas consistent with the rational dynamic asset pricing theory. In the existing behavioral finance option pricing formulas, the price process of the representative agent is not a semimartingale, which leads to arbitrage opportunities for the option seller. In the literatu…

AdaBoost cycles in probability simplex dynamics.

We propose and analyze numerically a simple dynamical model that describes the firm behaviors under uncertainty of demand forecast. Iterating this simple model and varying some parameters values we observe a wide variety of market dynamics such as equilibria, periodic and chaotic behaviors. Interestingly the model is a…

We study the phase transition of dynamical herd behaviors for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with three different values of the scaling exponent 3.11 (one time lag $τ$ = 1 minute), 2.81 (30 minut…

Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…

New algorithm learns switching dynamics from multiple neural signals.

The paper explores how structured representations influence learning dynamics in neural networks.

We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior an…

New method models complex dynamics using a base variable.

The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process p…

The aim of this paper is to show that the dynamics of $L^p$ heat semigroups ($p>2$) on a symmetric space of non-compact type is very different from the dynamics of the $L^p$ heat semigroups if $p\leq 2$. To see this, it is shown that certain shifts of the $L^p$ heat semigroups have a chaotic behavior if $p>2$ and that …

Paper tackles dynamic behavior of variable topology mechanisms, presenting new transition conditions.

Model explains herding and volatility in urban housing prices.

Modeling bank leverage dynamics to understand systemic risk in financial markets.

Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.

Wide networks with polynomial activations have proven asymptotic behavior.

Revisits behavioral finance option pricing model to align with rational asset pricing theory.

Gradient descent dynamics in neural networks show quenching and activation phases.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

This paper reveals periodic behavior in neural network training with BN and weight decay.

Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.

Meta-causal states group equivalent qualitative causal dynamics, useful for analyzing system changes.

Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner. Such behavior can be well-captured by structured, nonparametric edge-exchangeable graphs. However, such exchangeable models necessarily ig…

New method linearizes nonlinear coupled oscillators on graphs.

Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the …

AI agents in experimental markets exhibit behavioral patterns that aggregate into market dynamics.

Particle- and agent-based systems are a ubiquitous modeling tool in many disciplines. We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors w…

Paper proposes variational inference for piecewise-linear systems.

Research explores how interconnected systems synchronize and how to control their behavior.

The paper studies dynamical systems with evolving geometric structure using numerical methods.

Study of SGD with state-dependent noise, improving escape from local minima.

A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with near neighbor interactions. The evolution of each agent results from the competition between two factors: the agent's own tendency to grow and the environmen…

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is diffi…

We propose a simple stochastic model of market behavior. Dividing market participants into two groups: trend-followers and fundamentalists, we derive the general form of a stochastic equation of market dynamics. The model has two characteristic time scales: the time of changes of market environment and the characterist…

New RL framework simulates financial market dynamics.

Interprets how intrinsic motivation shapes behavior in RL agents.

We present a relatively detailed analysis of the persistence probability distributions in financial dynamics. Compared with the auto-correlation function, the persistence probability distributions describe dynamic correlations non-local in time. Universal and non-universal behaviors of the German DAX and Shanghai Index…

Study uses machine learning to predict nonlinear seismic brace behavior.

Random feature model shows slow self-correction of generalization gap.

Stylized facts can be regarded as constraints for any modeling attempt of price dynamics on a financial market, in that an empirically reasonable model has to reproduce these stylized facts at least qualitatively. The dynamics of market prices is modeled on a macro-level as the result of the dynamic coupling of two dyn…

Quantum circuits predict volatility dynamics preserving asymmetry.