The work examines the problem of researching economic processes in conditions of uncertainty and conflict between parties participating in them. It is emphasized that, from the point of view of mathematics, such research is usually reduced to solving a discrete antagonistic game of two players. Examples of specific economic situations are given, which lead to the need to use mathematical models based on matrix games, including the production behavior of firms (enterprises) both at the level of the product and at the level of its production. Special attention is paid to the relationship between matrix games and linear programming problems.
 For the implementation of mathematical models of matrix games, an approach is proposed, the basis of which is the transition to a pair of dual problems of linear programming, the further solution of each of which is carried out in the environment of the table processor MS Excel using a special add-in Solver. The organization of data on the MS Excel worksheet is described in detail, all the necessary formulas and conditions for finding the optimal solution are given.
 The proposed approach is illustrated by an example of solving the problem of choosing optimal strategies for the production of various types of products by an enterprise in conditions of uncertain demand. The obtained results are in good agreement with the data of other authors obtained by analytical methods. At the same time, one of the most attractive features of the proposed approach is the ease of computer implementation, solving the problem in data manipulation mode without the need for programming. There is also no need to simplify the problem by discarding the subordinate strategies of the players. Solving dual problems of linear programming makes it possible to calculate the mixed strategies of two players and contributes to making a reasonable management decision among the set of alternative solutions.
 The considered approach can be used for any firms that are engaged in the production of products and wish to reduce risks in conditions of uncertain demand.