A Formal Model for Adaptive Free Choice in Complex Systems

• URL: http://arxiv.org/abs/2011.06670v1
• Date: Thu, 12 Nov 2020 22:05:33 GMT
• Title: A Formal Model for Adaptive Free Choice in Complex Systems
• Authors: Ian T. Durham
• Abstract summary: I develop a formal model of free will for complex systems based on emergent properties and adaptive selection. The focus in this model is on the actual choices themselves viewed in the context of processes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, I develop a formal model of free will for complex systems based on emergent properties and adaptive selection. The model is based on a process ontology in which a free choice is a singular process that takes a system from one macrostate to another. I quantify the model by introducing a formal measure of the `freedom' of a singular choice. The `free will' of a system, then, is emergent from the aggregate freedom of the choice processes carried out by the system. The focus in this model is on the actual choices themselves viewed in the context of processes. That is, the nature of the system making the choices is not considered. Nevertheless, my model does not necessarily conflict with models that are based on internal properties of the system. Rather it takes a behavioral approach by focusing on the externalities of the choice process.

Related papers

• An Iterative Bayesian Approach for System Identification based on Linear Gaussian Models [86.05414211113627]
  We tackle the problem of system identification, where we select inputs, observe the corresponding outputs from the true system, and optimize the parameters of our model to best fit the data. We propose a flexible and computationally tractable methodology that is compatible with any system and parametric family of models.
  arXiv  Detail & Related papers  (2025-01-28T01:57:51Z)
• Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study [0.6999740786886538]
  We present a variability-aware ML algorithm selection approach that considers the commonalities and variations in the model selection process. The proposed approach can be seen as a step towards providing a more explicit, adaptive, transparent, interpretable, and automated basis for model selection.
  arXiv  Detail & Related papers  (2024-12-31T16:29:37Z)
• Stabilizing black-box model selection with the inflated argmax [8.52745154080651]
  This paper presents a new approach to stabilizing model selection that leverages a combination of bagging and an "inflated" argmax operation. Our method selects a small collection of models that all fit the data, and it is stable in that, with high probability, the removal of any training point will result in a collection of selected models that overlaps with the original collection. In both settings, the proposed method yields stable and compact collections of selected models, outperforming a variety of benchmarks.
  arXiv  Detail & Related papers  (2024-10-23T20:39:07Z)
• Differentiating Choices via Commonality for Multiple-Choice Question Answering [54.04315943420376]
  Multiple-choice question answering can provide valuable clues for choosing the right answer. Existing models often rank each choice separately, overlooking the context provided by other choices. We propose a novel model by differentiating choices through identifying and eliminating their commonality, called DCQA.
  arXiv  Detail & Related papers  (2024-08-21T12:05:21Z)
• Anytime Model Selection in Linear Bandits [61.97047189786905]
  We develop ALEXP, which has an exponentially improved dependence on $M$ for its regret. Our approach utilizes a novel time-uniform analysis of the Lasso, establishing a new connection between online learning and high-dimensional statistics.
  arXiv  Detail & Related papers  (2023-07-24T15:44:30Z)
• Dual policy as self-model for planning [71.73710074424511]
  We refer to the model used to simulate one's decisions as the agent's self-model. Inspired by current reinforcement learning approaches and neuroscience, we explore the benefits and limitations of using a distilled policy network as the self-model.
  arXiv  Detail & Related papers  (2023-06-07T13:58:45Z)
• Hidden variables, free choice, context-independence, and all that [0.0]
  The paper provides a systematic account of the hidden variable models (HVMs) formulated to describe systems of random variables with mutually exclusive contexts. The equivalence and unfalsifiability imply that freedom of choice and context-independent mapping are no assumptions at all. The conjunction of these two notions, however, defines a falsifiable HVM that describes noncontextuality when applied to systems with no disturbance or to consistifications of arbitrary systems.
  arXiv  Detail & Related papers  (2023-05-25T15:06:54Z)
• Choosing on Sequences [0.0]
  We propose a new framework that considers choice from infinite sequences. We show that bounded attention is due to the continuity of the choice functions with respect to a natural topology. We introduce the notion of computability of a choice function using Turing machines and show that computable choice rules can be implemented by a finite automaton.
  arXiv  Detail & Related papers  (2022-02-28T20:16:24Z)
• SMACE: A New Method for the Interpretability of Composite Decision Systems [3.1542695050861544]
  We present SMACE, Semi-Model-Agnostic Contextual Explainer, a novel interpretability method for decision systems. We show that established model-agnostic approaches produce poor results in this framework.
  arXiv  Detail & Related papers  (2021-11-16T19:37:35Z)
• Automated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory [0.19499120576896226]
  State-of-the-art process discovery methods construct free-choice process models from event logs. We propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques.
  arXiv  Detail & Related papers  (2021-06-26T21:14:04Z)
• Stein Variational Model Predictive Control [130.60527864489168]
  Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions. We show that this framework leads to successful planning in challenging, non optimal control problems.
  arXiv  Detail & Related papers  (2020-11-15T22:36:59Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)

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.