Modeling methods for strategy formulation in a turbulent environment

Abstract

The relevance of classical strategizing methods decreases in the era of a rapidly changing world, but this set of methods can be e panded through a variety of e ible modeling techni ues from other elds. Opera ons research, enterprise modeling, nowledge engineering, and ar cial intelligence have poten al to enrich strategizing process. These disciplines have generated a large number of suppor ng methods. We provide a classi ca on of the modeling methods by the degree of formaliza on and propose a conceptual framewor for the method choice.

Enhancing knowledge transferability: A contradiction of the codification approach

“…The frame of the knowledge model defines its aesthetical features. In this case, the structure serves as “social glue,” providing contact of various groups and individuals (Henderson, 1991). The robust structures are better for communication purposes as they increase the speed and quality of information exchange.…”

Artificial intelligence techniques in finance and financial markets: A survey of the literature

“…Models of AI, Knowledge Engineering and Operations research can be classified as generic, informal, semiformal, or formal (Gavrilova, Kubelskiy, Kudryavtsev, & Grinberg, 2018). An introductory book to AI divided AI into five fields: symbolic AI; artificial NNs (ANNs); evolutionary programming; cellular automata; and dynamical systems (Boden, 2018).…”

Artificial intelligence methods applied to financial assets price forecasting in trading contexts with low (intraday) and very low (high‐frequency) time frames

“…Artificial intelligence is used everywhere (Gavrilova, Kubelskiy, Kudryavtsev, & Grinberg, 2018). Recent research shows that artificial intelligence is an important subject that has tremendous impacts on financial markets (Tokic, 2018), where the use of machine learning methods can be applied to solve different problems.…”