Function-Coherent Gambles with Non-Additive Sequential Dynamics
- URL: http://arxiv.org/abs/2503.02889v1
- Date: Sat, 22 Feb 2025 14:58:20 GMT
- Title: Function-Coherent Gambles with Non-Additive Sequential Dynamics
- Authors: Gregory Wheeler,
- Abstract summary: We relax the additive combination axiom and introduce a nonlinear combination operator that effectively aggregates repeated gambles in the log-domain.<n>Our approach bridges the gap between expectation values and time averages and unifies normative theory with empirically observed non-stationary reward dynamics.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The desirable gambles framework provides a rigorous foundation for imprecise probability theory but relies heavily on linear utility via its coherence axioms. In our related work, we introduced function-coherent gambles to accommodate non-linear utility. However, when repeated gambles are played over time -- especially in intertemporal choice where rewards compound multiplicatively -- the standard additive combination axiom fails to capture the appropriate long-run evaluation. In this paper we extend the framework by relaxing the additive combination axiom and introducing a nonlinear combination operator that effectively aggregates repeated gambles in the log-domain. This operator preserves the time-average (geometric) growth rate and addresses the ergodicity problem. We prove the key algebraic properties of the operator, discuss its impact on coherence, risk assessment, and representation, and provide a series of illustrative examples. Our approach bridges the gap between expectation values and time averages and unifies normative theory with empirically observed non-stationary reward dynamics.
Related papers
- Function-coherent gambles [0.0]
This paper introduces function-coherent gambles, a generalization that accommodates non-linear utility.
We prove a representation theorem that characterizes acceptable gambles through continuous linear functionals.
We demonstrate how these alternatives to constant-rate exponential discounting can be integrated within the function-coherent framework.
arXiv Detail & Related papers (2025-02-22T14:44:54Z) - Transition of $α$-mixing in Random Iterations with Applications in Queuing Theory [0.0]
We show the transfer of mixing properties from the exogenous regressor to the response via coupling arguments.
We also study Markov chains in random environments with drift and minorization conditions, even under non-stationary environments.
arXiv Detail & Related papers (2024-10-07T14:13:37Z) - Learning Linear Causal Representations from Interventions under General
Nonlinear Mixing [52.66151568785088]
We prove strong identifiability results given unknown single-node interventions without access to the intervention targets.
This is the first instance of causal identifiability from non-paired interventions for deep neural network embeddings.
arXiv Detail & Related papers (2023-06-04T02:32:12Z) - Exact Non-Oblivious Performance of Rademacher Random Embeddings [79.28094304325116]
This paper revisits the performance of Rademacher random projections.
It establishes novel statistical guarantees that are numerically sharp and non-oblivious with respect to the input data.
arXiv Detail & Related papers (2023-03-21T11:45:27Z) - Work statistics, quantum signatures and enhanced work extraction in
quadratic fermionic models [62.997667081978825]
In quadratic fermionic models we determine a quantum correction to the work statistics after a sudden and a time-dependent driving.
Such a correction lies in the non-commutativity of the initial quantum state and the time-dependent Hamiltonian.
Thanks to the latter, one can assess the onset of non-classical signatures in the KDQ distribution of work.
arXiv Detail & Related papers (2023-02-27T13:42:40Z) - Causal Graph Discovery from Self and Mutually Exciting Time Series [10.410454851418548]
We develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program.
We demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs)
arXiv Detail & Related papers (2023-01-26T16:15:27Z) - Convex Analysis of the Mean Field Langevin Dynamics [49.66486092259375]
convergence rate analysis of the mean field Langevin dynamics is presented.
$p_q$ associated with the dynamics allows us to develop a convergence theory parallel to classical results in convex optimization.
arXiv Detail & Related papers (2022-01-25T17:13:56Z) - Sequential Kernel Embedding for Mediated and Time-Varying Dose Response
Curves [26.880628841819004]
We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression.
Our key innovation is a reproducing kernel Hilbert space technique called sequential kernel embedding.
arXiv Detail & Related papers (2021-11-06T19:51:39Z) - Causal Graph Discovery from Self and Mutually Exciting Time Series [12.802653884445132]
We develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program.
We demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs)
arXiv Detail & Related papers (2021-06-04T16:59:24Z) - Nonparametric Score Estimators [49.42469547970041]
Estimating the score from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models.
We provide a unifying view of these estimators under the framework of regularized nonparametric regression.
We propose score estimators based on iterative regularization that enjoy computational benefits from curl-free kernels and fast convergence.
arXiv Detail & Related papers (2020-05-20T15:01:03Z) - On dissipative symplectic integration with applications to
gradient-based optimization [77.34726150561087]
We propose a geometric framework in which discretizations can be realized systematically.
We show that a generalization of symplectic to nonconservative and in particular dissipative Hamiltonian systems is able to preserve rates of convergence up to a controlled error.
arXiv Detail & Related papers (2020-04-15T00:36:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.