Co-designing heterogeneous models: a distributed systems approach

• URL: http://arxiv.org/abs/2407.07656v1
• Date: Wed, 10 Jul 2024 13:35:38 GMT
• Title: Co-designing heterogeneous models: a distributed systems approach
• Authors: Marius-Constantin Ilau, Tristan Caulfield, David Pym,
• Abstract summary: This paper presents a modelling approach tailored for heterogeneous systems based on three elements. An inferentialist interpretation of what a model is, a distributed systems metaphor and a co-design cycle describe the practical design and construction of the model. We explore the suitability of this method in the context of three different security-oriented models.
• Score: 0.40964539027092917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nature of information security has been, and probably will continue to be, marked by the asymmetric competition of attackers and defenders over the control of an uncertain environment. The reduction of this degree of uncertainty via an increase in understanding of that environment is a primary objective for both sides. Models are useful tools in this context because they provide a way to understand and experiment with their targets without the usual operational constraints. However, given the technological and social advancements of today, the object of modelling has increased in complexity. Such objects are no longer singular entities, but heterogeneous socio-technical systems interlinked to form large-scale ecosystems. Furthermore, the underlying components of a system might be based on very different epistemic assumptions and methodologies for construction and use. Naturally, consistent, rigorous reasoning about such systems is hard, but necessary for achieving both security and resilience. The goal of this paper is to present a modelling approach tailored for heterogeneous systems based on three elements: an inferentialist interpretation of what a model is, a distributed systems metaphor to structure that interpretation and a co-design cycle to describe the practical design and construction of the model. The underlying idea is that an open world interpretation, supported by a formal, yet generic abstraction facilitating knowledge translation and providing properties for structured reasoning and, used in practice according to the co-design cycle could lead to models that are more likely to achieve their pre-stated goals. We explore the suitability of this method in the context of three different security-oriented models: a physical data loss model, an organisational recovery under ransomware model and an surge capacity trauma unit model.

Related papers

• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage. Models may behave unreliably due to poorly explored failure modes. causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Model-Free RL Agents Demonstrate System 1-Like Intentionality [16.427085062620215]
  We argue that model-free reinforcement learning agents exhibit behaviours that can be analogised to System 1 processes in human cognition. We propose a novel framework linking the dichotomy of System 1 and System 2 to the distinction between model-free and model-based RL.
  arXiv  Detail & Related papers  (2025-01-30T12:21:50Z)
• On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
  Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities. While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks. We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
  arXiv  Detail & Related papers  (2025-01-23T16:58:18Z)
• Modelling and Classification of Fairness Patterns for Designing Sustainable Information Systems [0.2867517731896504]
  This paper explores the concept of fairness in sociotechnical system design. It is based on a reference sustainability meta-model capturing the concepts of value, assumption, regulation, metric and task.
  arXiv  Detail & Related papers  (2024-11-26T21:23:56Z)
• A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data [77.34726150561087]
  This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. The model is able to produce fairly rich yet human-readable conceptual representations without training.
  arXiv  Detail & Related papers  (2023-07-16T15:59:13Z)
• Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes [72.13373216644021]
  We study the societal impact of machine learning by considering the collection of models that are deployed in a given context. We find deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available. These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.
  arXiv  Detail & Related papers  (2023-07-12T01:11:52Z)
• Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning [56.50123642237106]
  Common practice in model-based reinforcement learning is to learn models that model every aspect of the agent's environment. We argue that such models are not particularly well-suited for performing scalable and robust planning in lifelong reinforcement learning scenarios. We propose new kinds of models that only model the relevant aspects of the environment, which we call "minimal value-minimal partial models"
  arXiv  Detail & Related papers  (2023-01-24T16:40:01Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable Tradeoffs [0.0]
  We present a model-based and a data-driven strategy to generate surrogate models. The latter generates interpretable surrogate models by fitting artificial relations to a presupposed topological structure. Our framework is compatible with various spatial discretization schemes for distributed parameter models.
  arXiv  Detail & Related papers  (2022-02-02T17:07:02Z)
• On the Opportunities and Risks of Foundation Models [256.61956234436553]
  We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2021-08-16T17:50:08Z)
• Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities [0.0]
  We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture. We demonstrate the framework's efficacy in numerical experiments and illustrate its special merits in benchmarks and real-world applications.
  arXiv  Detail & Related papers  (2020-02-13T21:01:26Z)

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.