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Assessment of influence productivity in cognitive models

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Abstract

This article proposes a new influence productivity assessment methodology that is a cognitive intelligence system for the scenario planning of control impacts (generation and choice) for systems that are represented by directed weighted signed graphs based on the algorithm of effective controls. The algorithm implements a control model that expresses the direction of development (growth) of the system. The algorithm is based on the spectral properties of the adjacency matrix of a graph representing the model of a socioeconomic system and does not impose any constraints on the directions of the edges or the sign and weight range on the edges. Scenarios are assessed based on their compliance with tactical and strategic goals according to the codirectionality degree of the response vector with respect to the base vector of the model. The base vector is the effective control vector without constraints on the controls under the conditions of adequate model operation. The new methodology has three distinctive features: (1) the scenario approach is implemented with respect to a set of controls, (2) this approach is applicable for models with heterogeneous factors and does not require preliminary aggregation of the primary model elements of the system; and (3) this approach has a clear formalization metric for the selecting and generating of a set of control impacts. The process does not require the decision maker to have special mathematical training.

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Acknowledgements

This work was supported by the Southern Federal University.

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Authors and Affiliations

  1. Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Nekrasovskii, 44, Taganrog, Russia, 347900

    Alexander Tselykh & Vladislav Vasilev

  2. Department of Economics and Business, Chekhov Taganrog Institute, Rostov State University of Economics, Initsiativnaya, 48, Taganrog, Russia, 347900

    Larisa Tselykh

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  1. Alexander Tselykh
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  2. Vladislav Vasilev
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  3. Larisa Tselykh
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Correspondence to Larisa Tselykh.

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Appendix

Appendix

See Tables 8 and 9.

Table 8 Model factors of the cognitive map from the demonstration example
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Table 9 Results of computations by the AEC for the demonstration example
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Tselykh, A., Vasilev, V. & Tselykh, L. Assessment of influence productivity in cognitive models. Artif Intell Rev 53, 5383–5409 (2020). https://doi.org/10.1007/s10462-020-09823-8

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