scholarly journals Complex Scenarios with Competing Factors - A Conception Paper Applied to the COVID-19 Case

2020 ◽  
Author(s):  
Mauricio Pazini Brandão
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
General Result ◽  
Integral Form ◽  
Computational Simulations ◽  
Grand Total ◽  
Available Resources

Abstract A model to analyze complex systems facing threats with competing factors has been introduced. The Principle of Energy, in integral form, is used to conceive a theory in which competing factors dispute available resources to minimize undesirable outcomes. The general result indicates that the minimum response is obtained by a combination of the factors weighted by their corresponding criticalities. The theory has been applied to the case of the COVID-19 pandemic with two competing factors: Health and Economy. As result, to minimize the grand total number of deaths, the best recommendation is to balance the emphasis on both factors. The model can be generalized even further and may evolve from a qualitative to a quantitative status. In this evolution, it may allow for computational simulations and comparisons with field statistics for validation and forecasting. As such, this approach may become a useful tool for decision-making regarding resources allocations in order to reduce guessing in scenarios full of uncertainties.

scholarly journals Integrating Value Considerations in the Decision Making for the Design of Biorefineries

2020 ◽  
Vol 26 (6) ◽  
pp. 2927-2955
Author(s):  
Mar Palmeros Parada ◽  
Lotte Asveld ◽  
Patricia Osseweijer ◽  
John Alexander Posada
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Research And Development ◽  
Design Process ◽  
Design Decision ◽  
Value Sensitive Design ◽  
Design Practice ◽  
Novel Approach ◽  
Design Alternatives ◽  
Biobased Production

AbstractBiobased production has been promoted as a sustainable alternative to fossil resources. However, controversies over its impact on sustainability highlight societal concerns, value tensions and uncertainties that have not been taken into account during its development. In this work, the consideration of stakeholders’ values in a biorefinery design project is investigated. Value sensitive design (VSD) is a promising approach to the design of technologies with consideration of stakeholders’ values, however, it is not directly applicable for complex systems like biorefineries. Therefore, some elements of VSD, such as the identification of relevant values and their connection to a technology’s features, are brought into biorefinery design practice. Midstream modulation (MM), an approach to promoting the consideration of societal aspects during research and development activities, is applied to promote reflection and value considerations during the design decision making. As result, it is shown that MM interventions during the design process led to new design alternatives in support of stakeholders' values, and allowed to recognize and respond to emerging value tensions within the scope of the project. In this way, the present work shows a novel approach for the technical investigation of VSD, especially for biorefineries. Also, based on this work it is argued that not only reflection, but also flexibility and openness are important for the application of VSD in the context of biorefinery design.

scholarly journals SELF-ORGANIZATION IN COMPLEX SYSTEMS AS DECISION MAKING

2014 ◽  
Vol 17 (03n04) ◽  
pp. 1450016 ◽  
Author(s):  
V. I. YUKALOV ◽  
D. SORNETTE
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Mathematical Formulation ◽  
Conditional Entropy ◽  
Self Organization ◽  
Entropy Maximization ◽  
Natural Systems ◽  
Macroscopic States ◽  
System States ◽  
Statistical Systems

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.

Measuring Predictive Power

2014 ◽  
pp. 209-221
Keyword(s):  
Decision Making ◽  
Predictive Power ◽  
Final Model ◽  
Adequate Level ◽  
Accuracy Level ◽  
The One ◽  
Available Resources

This chapter explains common methods in evaluating model predictive power. If the goal is defined as finding the most important/risky customers, there are many different ways using the available resources. Analysts measure accuracy and look for answers. It is obvious that two different analysts would provide different models; however, what both are looking for is an adequate level of accuracy. That means that analysts have freedom while looking for models, but the final model needs to be accurate and usable for decision making. No matter what the final model is, the most important factors before the final results are confirmed are the model relevance tests. One can, for example, create several models with the same goal but using different methods or methodologies. The one with highest accuracy level is the best one. It is important to point out that models do not have to be based only on one method but can combine several methods at the same time.

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