Adaptive Learning and Multistage Compromises for Multilevel Decision Making in Macroeconomic Systems

1984 ◽  
pp. 23-53 ◽  
Author(s):  
D. F. Batten
Keyword(s):  
Decision Making ◽  
Adaptive Learning

Dynamic adaptive learning for decision-making supporting systems

2008 ◽  
Author(s):  
Haibo He ◽  
Yuan Cao ◽  
Sheng Chen ◽  
Sachi Desai ◽  
Myron E. Hohil
Keyword(s):  
Decision Making ◽  
Adaptive Learning

scholarly journals I judge you by your profit: Judgments of effort exerted by self and others are influenced by received rewards

2018 ◽  
Author(s):  
Max Rollwage ◽  
Franziska Pannach ◽  
Caedyn Stinson ◽  
Ulf Toelch ◽  
Igor Kagan ◽  
...  
Keyword(s):  
Decision Making ◽  
Adaptive Learning ◽  
Major Part ◽  
Information Sources ◽  
Computational Modelling ◽  
Cost Benefit ◽  
Social Contexts ◽  
The Other ◽  
Reward Information ◽  
Effort Rating

AbstractEffort constitutes a major part of cost-benefit calculations underlying decision making. Therefore, estimating the effort someone has spent on a task is a core dimension for evaluating own and others’ actions. It has been previously shown that self-judgments of effort are influenced by the magnitude of obtained rewards. It is unclear, however, whether the influence of reward on effort estimations is limited to self-judgments or whether reward incorporation represents a general computational principle when judging effort. Here we show that people also integrate reward magnitude when judging the effort exerted by others. Participants (N=48) performed an effortful sensorimotor task interleaved with a partner, while rating either their own or the other person’s effort. After each trial but before the effort rating, both participants were informed about the obtained reward. We found that higher rewards led to higher estimations of exerted effort, in self-as well as other-judgments, and this effect was more pronounced for other-judgments. In both types of judgment, computational modelling revealed that reward information and the perceived level of exertion were combined in a Bayes optimal manner to form effort estimates. Remarkably, the extent to which rewards influenced effort judgments was positively correlated with conservative world-views, indicating that the basic computations underlying this behavioural phenomenon might be related to more general beliefs about the association between effort and reward in the society. The integration of reward information into retrospective effort judgments underscores the convergence of multiple information sources that supports adaptive learning and decision making in social contexts.

scholarly journals Decision Science for Future Earth: A Conceptual Framework

2021 ◽  
pp. 3-64
Author(s):  
Tetsukazu Yahara ◽  
Wataru Tanaka ◽  
Yukako Inoue ◽  
Jounghun Lee ◽  
Kun Qian ◽  
...  
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Conceptual Framework ◽  
Adaptive Learning ◽  
Evolutionary Biology ◽  
Cognitive Biases ◽  
Local Communities ◽  
Decision Science ◽  
Global Trends ◽  
Success Stories

AbstractThe purpose of this chapter is to review progress in our understanding of human behavior and decision-making relevant to future earth research agenda, and propose Decision Science as a hub of knowledge networks connecting disciplinary and interdisciplinary sciences with the practice of problem-solving. This review is composed of four sections. First, we describe the conceptual framework of “decision science for a sustainable society” and argue that evolutionary biology of the human nature is key to construct this framework. Second, we review how our group decision-making often fails due to various cognitive biases and argue that participatory approaches of co-design and co-production do not guarantee reasonable decision-making. Third, we review success stories of problem-solving in local communities and consider how we can connect those successes in local communities to successful national and global decision-making. Fourth, learning from both failures and successes, we argue that the adaptive learning of society is a process enabling us to transform our society toward a sustainable future. We review some positive global trends toward sustainability and consider the cognitive processes and behavioral mechanisms behind those trends that would provide clues for finding successful ways to transform our society.

Modeling Multiparty Competition

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Making ◽  
Adaptive Learning ◽  
Party System ◽  
Decision Rules ◽  
Agent Based Modeling ◽  
Party Competition ◽  
Strategic Analysis ◽  
New Approach ◽  
Agent Based ◽  
Autonomous Decision

This chapter begins with a brief discussion of the need for a new approach to modeling party competition. It then makes a case for the use of agent-based modeling to study multiparty competition in an evolving dynamic party system, given the analytical intractability of the decision-making environment, and the resulting need for real politicians to rely on informal decision rules. Agent-based models (ABMs) are “bottom-up” models that typically assume settings with a fairly large number of autonomous decision-making agents. Each agent uses some well-specified decision rule to choose actions, and there may be considerable diversity in the decision rules used by different agents. Given the analytical intractability of the decision-making environment, the decision rules that are specified and investigated in ABMs are typically based on adaptive learning rather than forward-looking strategic analysis, and agents are assumed to have bounded rather than perfect rationality. An overview of the subsequent chapters is also presented.

ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model

2019 ◽  
Vol 16 (4) ◽  
pp. 53-73
Author(s):  
Abdulrahman Elhosuieny ◽  
Mofreh Salem ◽  
Amr Thabet ◽  
Abdelhameed Ibrahim
Keyword(s):  
Decision Making ◽  
Adaptive Learning ◽  
Execution Time ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Matrix Multiplication ◽  
Computation Offloading ◽  
Phase Curve ◽  
Predicting Model ◽  
Major Interest

Nowadays, mobile computation applications attract major interest of researchers. Limited processing power and short battery lifetime is an obstacle in executing computationally-intensive applications. This article presents a mobile computation automatic decision-making offloading framework. The proposed framework consists of two phases: adaptive learning, and modeling and runtime computation offloading. In the adaptive phase, curve-fitting (CF) technique based on non-linear polynomial regression (NPR) methodology is used to build an approximate time-predicting model that can estimate the execution time for spending the processing of the detected-intensive applications. The runtime computation phase uses the time predicting model for computing the predicted execution time to decide whether to run the application remotely and perform the offloading process or to run the application locally. Eventually, the RESTful web service is applied to carry out the offloading task in the case of a positive offloading decision. The proposed framework experimentally outperforms a competitive state-of-the-art technique by 73% concerning the time factor. The proposed time-predicting model records minimal deviation of the originally obtained values as it is applied 0.4997, 8.9636, 0.0020, and 0.6797 on the mean squared error metric for matrix-determinant, image-sharpening, matrix-multiplication, and n-queens problems, respectively.

Fuzzy adaptive learning model of decision-making process

1986 ◽  
Vol 18 (3) ◽  
pp. 273-282 ◽  
Author(s):  
A.F. Blishun
Keyword(s):  
Decision Making ◽  
Adaptive Learning ◽  
Learning Model ◽  
Decision Making Process ◽  
Fuzzy Adaptive

scholarly journals Developing Adaptive Learning Environments to Support Long-Term Health Promotion

2018 ◽  
Vol 13 (1) ◽  
pp. 30-32
Author(s):  
Tabbetha Lopez ◽  
Katherine R. Arlinghaus ◽  
Craig A. Johnston
Keyword(s):  
Decision Making ◽  
Health Promotion ◽  
Disease Prevention ◽  
Learning Environments ◽  
Adaptive Learning ◽  
Health Decisions ◽  
Unhealthy Behavior ◽  
Promotion Strategies ◽  
The Creation

Health promotion strategies typically include changing the environment, providing supervision to decrease the likelihood an unhealthy behavior will occur, and increasing skills to make decisions supporting health in environments in which such choices are challenging to make. The first two strategies are important in improving the environment to promote healthy decision making. However, the creation of restrictive environments has repeatedly shown to not support disease prevention in the long term. Restrictive environments do not support the development of skills to make healthy choices when restrictions are not in place. This is particularly true for children who are learning to navigate their environment and make health decisions. The creation of adaptive learning environments should be prioritized to help individuals develop the skills needed for long-term health promotion.

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