Searching for effective and efficient way of knowledge transfer within an organization

In this paper three models of knowledge transfer in organization are considered. In the first model (A) the transfer of chunks of knowledge among agents is possible only when the sender has exactly one more chunks of knowledge than recipient. This is not dissimilar with bounded confidence model of opinion dynamics. In the second model (B) the knowledge transfer take place when sender is "smarter" than recipient. Finally, in the third scenario (model C) we allow for knowledge transfer also when sender posses the same or greater number of chunks of knowledge as recipient. The simulation bases on cellular automata technique. The organization members occupy nodes of square lattice and they interact only with their nearest neighbors. With computer simulations we show, that the efficiency and the effectiveness of knowledge transfer i) for model C is better than for model B ii) and it is worse for model A than for model B.Comment: 8 pages, 5 figures (in 19 files), for 10th International Conference on Agents and Artificial Intelligenc

The Study Of Properties Of The Word Of Mouth Marketing Using Cellular Automata

This article presents the possibility of using cellular automata, to study the properties of word of mouth (w-o-m) marketing. Cellular automata allow to analyze the dynamics of changes in views and attitudes in social groups based on local interactions between people in small groups of friends, family members etc. The proposed paper shows the possibility of modelling the dynamics of word of mouth mechanism, if the basic assumptions of this process are: different size groups where this phenomenon occurs, and varied access to information. On the competing firms market, the dependence of the w-o-m mechanism dynamics on the model parameters is show

Noise induced unanimity and disorder in opinion formation.

We propose an opinion dynamics model based on Latané's social impact theory. Actors in this model are heterogeneous and, in addition to opinions, are characterised by their varying levels of persuasion and support. The model is tested for two and three initial opinions randomly distributed among actors. We examine how the noise (randomness of behaviour) and the flow of information among actors affect the formation and spread of opinions. Our main research involves the process of opinion formation and finding phases of the system in terms of parameters describing noise and flow of the information for two and three opinions available in the system. The results show that opinion formation and spread are influenced by both (i) flow of information among actors (effective range of interactions among actors) and (ii) noise (randomness in adopting opinions). The noise not only leads to opinions disorder but also it promotes consensus under certain conditions. In disordered phase and when the exchange of information is spatially effectively limited, various faces of disorder are observed, including system states, where the signatures of self-organised criticality manifest themselves as scale-free probability distribution function of cluster sizes. Then increase of noise level leads system to disordered random state. The critical noise level above which histograms of opinion clusters' sizes loose their scale-free character increases with increase of the easy of information flow

The Ukrainian Economy Transformation into the Circular Based on Fuzzy-Logic Cluster Analysis

In the era of limited resources and progressive environmental degradation, the circular economy is a practical application of sustainable development. It is an alternative, but also competitive way to achieve economic growth in accordance with the principles of sustainable development. This issue was considered in this paper in the context of the Ukrainian economy. The Ukrainian economy’s transformation into a circular one needs to find ways to choose practical tools for such a transition, considering the destructive impact of economic activities on the environment, population, and economy. The goal was to develop a method of choosing tools for the circular transformations of economic activities for each cluster and to reduce man-made damage to the environment. Cluster analysis, fuzzy C-means method, and grouping of economic activities were used. Two analyzed sectors turned out to be the most interesting: mining and quarrying, and electricity, gas, steam, and air conditioning supply, which were finally assigned to the cluster with a high level of destructive impact, defined as ‘environmentally unfriendly’. The proposed method allows the choice of circular transformation tools for economic activities depending on the destructive impact of these economic activities within each cluster

Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors

Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors

Automatic Identification of Sound Source Position Coordinates Using a Sound Metric System of Sensors Linked with an Internet Connection

In this article, we deal with the problem of increasing the accuracy of the automatic determination of the coordinates of the sound source location. We propose a new algorithm for the identification of the sound source’s position coordinates based on a system of three equations of the second order describing the dynamics of acoustic wavefront propagation. The implementation of the algorithm is carried out by a distributed automated system, which includes autonomous sensor-receivers located in the field and connected to the server of this system via wireless communication channels. Sensor-receivers are placed at the vertices of a flat, symmetrical figure with 4 axes of symmetry of the second order (square). The proposed algorithm takes into account the change in the phase speed of the sound wave when the temperature, air humidity, wind direction and speed change and allows for the determination of the coordinates of the position of the sound source with an error of no more than 1%. The experiment with real input data was carried out in a simulated environment, which was created on the Node.js platform