Looking Through Glass Box

Alexis Kafantaris

cs.NEMar 6, 2026

Looking Through Glass Box

Authors: Alexis Kafantaris

Organizations: Athens University of Economics and Business

Abstract

This essay is about a neural implementation of the fuzzy cognitive map, the FHM, and corresponding evaluations. Firstly, a neural net has been designed to behave the same way that an FCM does; as inputs it accepts many fuzzy cognitive maps and propagates them in order to learn causality patterns. Moreover, the network uses langevin differential Dynamics, which avoid overfit, to inverse solve the output node values according to some policy. Nevertheless, having obtained an inverse solution provides the user a modification criterion. Having the modification criterion suggests that information is now according to discretion as a different service or product is a better fit. Lastly, evaluation has been done on several data sets in order to examine the networks performance.

Figures & tables

Experiment / Topology	Nodes	Direct Edge Acc.	Transitive Chain Acc.
Base Urban Policy	9	$99.29\%\pm 2.20\%$	$99.38\%\pm 1.92\%$
Extended Urban Policy	14	$90.89\%\pm 3.75\%$	$85.91\%\pm 6.06\%$
Ministry Urban Policy*	19	$87.50\%\pm 5.49\%$	$74.64\%\pm 6.05\%$
Expanded Urban Policy	24	$86.49\%\pm 3.69\%$	$76.13\%\pm 4.87\%$
Sachs Protein Network	11	$76.83\%\pm 8.20\%$	$75.00\%\pm 13.46\%$
Sachs Protein Network	25	$74.60\%\pm 8.56\%$	$63.50\%\pm 9.10\%$

Table 1: Summary of Causal Evaluation Experiments

Fuzzy Cognitive Maps

See all 8 papers

LLM for the development of FCM
Jul 6, 2026Alexis KafantarisFuzzy Cognitive Maps
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
May 18, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoComputational Cognitive Modeling Bayesian Inference
University of Southern California, Los Angeles, CA 90007, USA · Florida International University, Miami, FL 33199, USA
Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators
Sep 14, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoCausal World Models Fuzzy Cognitive Maps
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA · School of Computing and Information Sciences, Florida International University Miami, FL 33199, USA

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LLM for the development of FCM

Alexis Kafantaris

Fuzzy Cognitive Maps

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This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as prompt input and determine relevant quantitative data as the model output. In turn, this output can be utilized for the construction of a data driven fuzzy cognitive map. Hence, this implementation is achieved and then the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor. Furthermore, the extracted documents pass through the model unfiltered and then a fuzzy cognitive map is trained and evaluated. A case is made about Greek reviews where a star topology FCM is formed that indicates the preferences of the reviewers. Finally, external validation is performed to establish whether the fuzzy cognitive map can correlate the star rating of the review -an outcome outside the model's inference scope -with its predicted satisfaction.

Abstract

Fuzzy Cognitive Maps

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