scholarly journals Mathematical modelling of the future for complex systems

1982 ◽  
Vol 3 (2) ◽  
pp. 153-160 ◽  
Author(s):  
M. Shahinpoor
Keyword(s):  
Complex Systems ◽  
Mathematical Modelling ◽  
The Future

How Is the Future Unknown?

2020 ◽  
Author(s):  
Martijn van der Steen ◽  
Mark van Twist
Keyword(s):  
Complex Systems ◽  
Organizational Strategies ◽  
The Future ◽  
Uncertain Future

The future is inherently uncertain. However, most policies are deliberate attempts to anticipate the future and to change and shape the future in an intended way. This chapter provides concepts for three key elements that are necessary to prepare for an unknown future. First, it conceptualizes what makes the future uncertain; uncertainty does not stem from the amount of time itself, but rather from the dynamics that can play out in that time. That is why it matters significantly if a system is complex or complicated; complex systems are much more dynamic and unpredictable, and complicated systems are much more stable and predictable. Second, there are different approaches for “studying” the dynamics; forecasting and foresight depart from entirely different angles of looking at the future, and both have their own strengths and weakness. Third, there are different organizational strategies for preparing for an unknown future; robustness, resilience, and adaptivity are three possible principles for organizing and preparing for uncertainty. In order to prepare for an uncertain future, or to study the uncertain future, scholars and policymakers should systematically take these three essential steps into account; how is the future unknown, how do we study the future, and what concept for anticipation do we apply here?

scholarly journals AUTOMATED MODELING AND FORECASTING SYSTEM OF ENTERPRISE ACTIVITIES

2021 ◽  
Vol 23 ◽  
pp. 20-26
Author(s):  
A. Khariv ◽  
A. Lagun
Keyword(s):  
Mathematical Modelling ◽  
Mathematical Models ◽  
Computer Technology ◽  
Rapid Development ◽  
Computing Methods ◽  
Automated System ◽  
Enterprise Management ◽  
Mathematical Methods ◽  
The Future ◽  
Modelling And Forecasting

Introduction. Mathematical methods and models are an effective tool for studying complex economic systems at different levels of enterprise management. Economic mathematical modelling is actively developing not only as a scien-tific field but also as a means of substantiating management decisions in business, in the analysis and forecasting of socio-economic processes and phenomena. In the arsenal of economic and mathematical modelling now are using- modern computing methods and computer technology. Libraries of economic and mathematical models are an integral part of the architecture of decision support systems in specific areas of the economy. The rapid development of computer technology stimulates the emergence and formation of new theoretical volumes and applied areas of modelling.Purpose. Like any large and complex field of knowledge, mathematical modelling is evolving in different direc-tions, acquiring new flexible research methods. Therefore, based on new hardware, technology and software platforms it is necessary to create new information systems using economic and mathematical models in particular for forecasting of enterprise activities.Results. The article analyses the methods of modelling and forecasting the enterprise, considers the principles of software design. Using systems analysis, the design problem was analytically divided into parts. Also were investigated the connections and relationships between these parts, in particular, were implemented the problem tree and the goal tree. Implemented business process modelling performs based on created structural-logical diagrams, namely the IDEF0 dia-gram, which helps to visually display data and information that affect software development, a server part, input data and users. Using the results of research, the authors developed an automated information system for modelling and forecasting the activities of the enterprise, which uses models of Holt, Brown, exponential smoothing and Box-Jenkins for modelling. Part of the developed system is a designed software product that implements the objectives of the research. The obtained program results allow creating a clear forecast of the future activities of the enterprise.Conclusions. Based on the built graphs of modelling and forecasting of the Cisco Systems company financial activity with using of the developed automated system, we established that the Brown model is the best for providing educational sampling and a forecast of activity. The development of the automated system in the future involves the expansion of functionality, improvement and increasing of quality, as well as the creation of powerful analytics for more detailed forecasting.

Crossing the Chasm

2012 ◽  
pp. 280-289
Author(s):  
Theresa M. Vitolo
Keyword(s):  
Complex Systems ◽  
Serious Games ◽  
Social Economic ◽  
Serious Game ◽  
Scientific Methods ◽  
Current State ◽  
Gaming Technology ◽  
The Future

Serious games are technology with unrealized potential as an innovation for reasoning about complex systems. The technology is enticing to technologically-savvy individuals, but the acceptance of serious games into mainstream processes requires addressing several systemic issues spanning social, economic, behavioral, and technological aspects. First, deployment of gaming technology for critical processes needs to embrace statistical and scientific methods appropriate for valid, accurate, and verifiable simulation of such processes. Second, identifying the correct instance and application breadth for a serious game within an organization needs to be articulated and supported with research. Third, funding for serious-games initiatives will need to be won as the funding will displace monies previously allocated and championed for other projects. Last, the endeavor faces the problem of negative connotations about its appropriateness as a viable technology for mainstream processes rather than for entertainment and diversion. The chapter examines the chasm serious games must traverse by examining the issues and posing approaches to minimize their effect on the adoption of the technology. The histories of other technologies that faced similar hurdles are compared to the current state of serious games, offering a perspective on the hurdle’s resolution. In the future, the hurdles can be minimized as curricula are developed with the solutions to the issues incorporated in the content.

Types of scientific rationality in aspects of the three paradigm

2015 ◽  
Vol 4 (1) ◽  
pp. 22-30
Author(s):  
Карпин ◽  
V. Karpin ◽  
Живогляд ◽  
R. Zhivoglyad ◽  
Гудкова ◽  
...  
Keyword(s):  
Complex Systems ◽  
Basic Principle ◽  
Self Organization ◽  
Scientific Rationality ◽  
Basic Principles ◽  
The Third ◽  
Self Development ◽  
The Future

Since the release of the well-known work of W. Weaver «Science and Complexity» (1948) only V.S. Stepin had taken some significant efforts to develop the doctrine of the three types of systems in nature. In this case, the main achievements of V.S. Stepin in postnonclassic reduced to two fundamental results: violation of the basic principle of T. Kuhn´s contradictions when changing paradigms (V.S. Stepin shows the effect of «investments», when complex systems operate classical and nonclassical rationality simultaneously) and repeated emphasis on the possibility of «change ... the probability of emerging of other (the system) conditions». At the same time, V.S. Stepin in his last works (monographs) identified a particular role of self-organization and self-development in case of complex biosocial systems. All this in theory of chaos and self-organization form 5 basic principles of functioning of complexity (or systems of the third type - STT). In fact, V.S. Stepin laid the foundation for the future (new) philosophy and developed now theory of chaos and self-organization in which humanity moved into the area of uncertainty of living (social in particular) systems completely. However, the rationality of the third type (postnonclassic) requires corrections and additions, as shown in a number of monographs of V.S. Stepin.

scholarly journals Partitioning prediction uncertainty in climate-dependent population models

2016 ◽  
Vol 283 (1845) ◽  
pp. 20162353 ◽  
Author(s):  
Gilles Gauthier ◽  
Guillaume Péron ◽  
Jean-Dominique Lebreton ◽  
Patrick Grenier ◽  
Louise van Oudenhove
Keyword(s):  
Complex Systems ◽  
Environmental Stochasticity ◽  
Population Increase ◽  
Demographic Model ◽  
Sampling Variance ◽  
Structural Uncertainty ◽  
Climate Scenarios ◽  
Prediction Uncertainty ◽  
The Future ◽  
Process Variance

The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate–demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose ( Chen caeruslescens atlantica ), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.

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