Predicting and Shaping or How to Close the Future

2020 ◽  
Vol 11 (2020) ◽  
Author(s):  
Christina Vagt
Keyword(s):  
Complex Systems ◽  
General Problem ◽  
Computer Engineering ◽  
Social Environments ◽  
Strategic Response ◽  
Computer Technologies ◽  
The Future ◽  
Behavioral Design

Behavioral design of so called »persuasive computer technologies« is the result of a merger between psychology, economics, and computer engineering. The article discusses its genealogy from the strategic response of military, governmental, and academic players to the general problem that the behavior of complex systems such as humans, societies, or markets is difficult to predict, and that controlling these complex systems means shaping them by designing their technological and social environments.

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?

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.

scholarly journals AI Safety and General Collective Intelligence

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
The Other ◽  
General Intelligence ◽  
Human Beings ◽  
Artificial General Intelligence ◽  
The Future ◽  
Ai Safety

Considering both current narrow AI, and any Artificial General Intelligence (AGI) that might be implemented in the future, there are two categories of ways such systems might be made safe for the human beings that interact with them. One category consists of mechanisms that are internal to the system, and the other category consists of mechanisms that are external to the system. In either case, the complexity of the behaviours that such systems might be capable of can rise to the point at which such measures cannot be reliably implemented. However, General Collective Intelligence or GCI can exponentially increase the general problem-solving ability of groups, and therefore their ability to manage complexity. This paper explores the specific cases in which AI or AGI safety cannot be reliably assured without GCI.

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