Suchergebnisse

Rethinking rationality / Gerd Gigerenzer and Reinhard Selten -- What is bounded rationality? / Reinhard Selten -- The adaptive toolbox / Gerd Gigerenzer -- Fast and frugal heuristics for environmentally bounded minds / Peter M. Todd -- Evolutionary adaptation and the economic concept of bounded rationality--a dialogue / Peter Hammerstein -- Group report : Is there evidence for an adaptive toolbox? / Abdolkarim Sadrieh ... [et al.] -- The fiction of optimization / Gary Klein -- Preferential choice and adaptive strategy use / John W. Payne and James R. Bettman -- Comparing fast and frugal heuristics and optimal models / Laura Martignon -- Group report : Why and when do simple heuristics work? / Daniel G. Goldstein ... [et al.] -- Emotions and cost-benefit assessment : the role of shame and self-esteem in risk taking / Daniel M.T. Fessler -- Simple reinforcement learning models and reciprocation in the prisoner's dilemma game / Ido Erev and Alvin E. Roth -- Imitation, social learning, and preparedness as mechanisms of bounded rationality / Kevin N. Laland -- Decision making in superorganisms : how collective wisdom arises from the poorly informed masses / Thomas D. Seeley --Group report : Effects of emotions and social processes on bounded rationality / Barbara A. Mellers ... [et al.] -- Norms and bounded rationality / Robert Boyd and Peter J. Richerson -- Prominence theory as a tool to model boundedly rational decisions / Wulf Albers -- Goodwill accounting and the process of exchange / Kevin A. McCabe and Vernon L. Smith -- Group report : What is the role of culture in bounded rationality? / Joseph Henrich ... [et al.]

AbstractMany compelling examples have recently been provided in which people can achieve impressive epistemic success, e.g. draw highly accurate inferences, by using simple heuristics and very little information. This is possible by taking advantage of the features of the environment. The examples suggest an easy and appealing naturalization of rationality: on the one hand, people clearlycanapply simple heuristics, and on the other hand, they intuitivelyoughtdo so when this brings them high accuracy at little cost.. The 'ought-can' principle is satisfied, and rationality is meaningfully normative. We show, however, that this naturalization program is endangered by a computational wrinkle in the adaptation process taken to be responsible for this heuristics-based ('ecological') rationality: for the adaptation process to guarantee even minimal rationality, it requires astronomical computational resources, making the problem intractable. We consider various plausible auxiliary assumptions in attempt to remove this obstacle, and show that they do not succeed; intractability is a robust property of adaptation. We discuss the implications of our findings for the project of naturalizing rationality.

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- 1 How We Define Machine Learning -- 2 Preliminaries -- 2.1 Data and Dataset Types -- 2.1.1 Cross-Sectional -- 2.1.2 Time-Series -- 2.1.3 Panel -- 2.2 Plots -- 2.3 Probability Distributions with R -- 2.4 Regressions -- 2.4.1 Ordinary Least Squares (OLS) -- 2.4.2 Maximum Likelihood Estimators -- 2.4.3 Estimating MLE with R -- 2.5 BLUE -- 2.6 Modeling the Data -- 2.7 Causal vs. Predictive Models -- 2.7.1 Causal Models -- 2.7.2 Predictive Models -- 2.8 Simulation -- Part 1 Formal Look at Prediction -- 3 Bias-Variance Tradeoff -- 3.1 Estimator and MSE -- 3.2 Prediction - MSPE -- 3.3 Biased Estimator as a Predictor -- 3.4 Dropping a Variable in a Regression -- 3.5 Uncertainty in Estimations and Predictions -- 3.6 Prediction Interval for Unbiased OLS Predictor -- 4 Overfitting -- Part 2 Nonparametric Estimations -- 5 Parametric Estimations -- 5.1 Linear Probability Models (LPM) -- 5.2 Logistic Regression -- 5.2.1 Estimating Logistic Regression -- 5.2.2 Cost Functions -- 5.2.3 Deviance -- 5.2.4 Predictive Accuracy -- 6 Nonparametric Estimations - Basics -- 6.1 Density Estimations -- 6.2 Kernel Regressions -- 6.3 Regression Splines -- 6.4 MARS - Multivariate Adaptive Regression Splines -- 6.5 GAM - Generalized Additive Model -- 7 Smoothing -- 7.1 Using Bins -- 7.2 Kernel Smoothing -- 7.3 Locally Weighted Regression loess() -- 7.4 Smooth Spline Regression -- 7.5 Multivariate Loess -- 8 Nonparametric Classifier - kNN -- 8.1 mnist Dataset -- 8.2 Linear Classifiers (again) -- 8.3 k-Nearest Neighbors -- 8.4 kNN with Caret -- 8.4.1 mnist_27 -- 8.4.2 Adult Dataset -- Part 3 Self-Learning -- 9 Hyperparameter Tuning -- 9.1 Training, Validation, and Test Datasets -- 9.2 Splitting the Data Randomly -- 9.3 k-Fold Cross-Validation -- 9.4 Cross-Validated Grid Search.

Selective pressures favoring rapid decisions would have led to the evolution of simple decision-making mechanisms that could take the form of heuristics and rules that use as little available information as possible. Such decision heuristics can only be ecologically rational—yielding accurate inferences in particular problem domains—if they exploit the way that information is structured in the environment. The author presents a variety of fast and frugal heuristics that are ecologically rational and shows how they can be organized in the mind's adaptive toolbox of decision-making strategies.

This article accounts for the EU's failure to implement a strategy of Conflict Transformation (CT) through Regional Integration (RI) in four selected regions: Latin America, Asia, Africa, and the Middle East and North Africa (MENA). As a normative power, the EU has traditionally acted as a promoter of regional integration, with an underlying view of reducing regional conflicts. The academic literature, however, has failed to assess the exact role of the 'RI-CT nexus'1 in the EU's foreign policy toolbox, as well as its outcomes. To fill this gap, our study investigates the degree to which the RI-CT strategy is perceived by EU practitioners as a key deliverable and what concrete measures or influence paths have been implemented by the EU. We show that the EU has made use of four main types of influence paths in order to transform regional conflict dynamics. Nevertheless, our findings also demonstrate that RI developments within the different regions and, in particular, negative regional responses, progressively led EU practitioners to adapt and develop a behaviour of self-restraint.

To understand mental self-government of the developing reading and writing brain, correlations of clustering coefficients on fMRI reading or writing tasks with BASC 2 Adaptivity ratings (time 1 only) or working memory components (time 1 before and time 2 after instruction previously shown to improve achievement and change magnitude of fMRI connectivity) were investigated in 39 students in grades 4 to 9 who varied along a continuum of reading and writing skills. A Philips 3T scanner measured connectivity during six leveled fMRI reading tasks (subword—letters and sounds, word—word-specific spellings or affixed words, syntax comprehension—with and without homonym foils or with and without affix foils, and text comprehension) and three fMRI writing tasks—writing next letter in alphabet, adding missing letter in word spelling, and planning for composing. The Brain Connectivity Toolbox generated clustering coefficients based on the cingulo-opercular (CO) network; after controlling for multiple comparisons and movement, significant fMRI connectivity clustering coefficients for CO were identified in 8 brain regions bilaterally (cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, insula, cingulum—cingulate gyrus, and cingulum—hippocampus). BASC2 Parent Ratings for Adaptivity were correlated with CO clustering coefficients on three reading tasks (letter-sound, word affix judgments and sentence comprehension) and one writing task (writing next letter in alphabet). Before instruction, each behavioral working memory measure (phonology, orthography, morphology, and syntax coding, phonological and orthographic loops for integrating internal language and output codes, and supervisory focused and switching attention) correlated significantly with at least one CO clustering coefficient. After instruction, the patterning of correlations changed with new correlations emerging. Results show that the reading and writing brain's mental government, supported by both CO ...

Chapter 1 Introduction: The Goal and Contents of This Dissertation 1 ECOLOGICALLY RATIONAL HEURISTICS 2 ACT-R: A UNIFIED THEORY OF COGNITION 5 ORGANIZATION OF THE DISSERTATION 8 Chapter 2 Methodological Preliminaries 10 INTRODUCTION 10 OVERVIEW OF THE CHAPTER AND INTRODUCTORY DEFINITIONS 11 HOW TO SELECT BETWEEN FORMAL MODELS: A SHORT OVERVIEW 12 CONCLUSION 18 Chapter 3 How Memory Aids Strategy Selection 19 ABSTRACT 19 INTRODUCTION 19 HOW MEMORY AIDS STRATEGY SELECTION: THE HYPOTHESIS OF NON-OVERLAPPING COGNITIVE NICHES 21 JUDGMENTS FROM THE ACCESSIBILITY OF MEMORIES 22 THE ADAPTIVE TOOLBOX 23 OVERVIEW OF THE CHAPTER 25 AN ACT-R MODEL OF NON-OVERLAPPING COGNITIVE NICHES 26 OVERVIEW OF SIMULATION STUDIES 1 AND 2 33 HOW I CALIBRATED THE MODEL TO PREDICT MEMORY RETRIEVAL (SIMULATION STUDY 1) 33 TESTS OF THE TARTLE NICHE AND TARTLE–KNOWLEDGE NICHE HYPOTHESES (SIMULATION STUDY 2) 42 WHEN WOULD A PERSON USING THE FLUENCY HEURISTIC MAKE ACCURATE INFERENCES? (EXPERIMENTS 1–6; SIMULATION STUDIES 3, 4) 44 ACCURACY AS A DETERMINANT OF STRATEGY SELECTION (EXPERIMENT 7) 53 EFFORT AS A DETERMINANT OF STRATEGY SELECTION (EXPERIMENT 8) 58 TIME AS A DETERMINANT OF STRATEGY SELECTION (EXPERIMENT 9) 62 GENERAL DISCUSSION 67 CONCLUDING REMARKS: EMERGING COGNITIVE NICHES 72 Chapter 4 Models of Recognition-based Multi-alternative Inference 74 ABSTRACT 74 INTRODUCTION 74 GENERALIZING THE RECOGNITION HEURISTIC: ELIMINATION BY RECOGNITION 75 ALTERNATIVE MODELS OF INFERENCE: A COMPETITION 76 TOWARD A THEORY OF STRATEGY SELECTION BY DEFAULT 79 OVERVIEW OF THE EXPERIMENTS: RECOGNITION IN POLITICAL ELECTIONS 82 DO CONFLICTING CUES OVERRULE THE RELIANCE ON RECOGNITION? (EXPERIMENT 11; REANALYSIS OF EARLIER ELECTION STUDY) 83 GENERALIZING THE RECOGNITION HEURISTIC: HOW EPISODIC KNOWLEDGE AIDS USING RECOGNITION (EXPERIMENT 12) 89 IS A LACK OF KNOWLEDGE INFORMATIVE ABOUT THE PREDICTIVE ACCURACY OF RECOGNITION? (EXPERIMENT 13) 98 HOW WELL DO COMPENSATORY MODELS PREDICT INDIVIDUAL INFERENCES? (EXPERIMENT 14; REANALYSIS OF EXPERIMENT 7) 102 DO ...

This thesis explores the role of adaptive learning in explaining empirical phenomena such as the Great Inflation. We show that the historic monetary policy deviated from the optimally recommended policy under learning. These deviations are caused by Brainard-type uncertainty which induces a conservative policy stance. Accounting for both imperfect knowledge and estimation uncertainty, the optimal policy under adaptive learning is consistent with historic policy behavior. However, we show that an optimizing but informationally constrained policy maker would most likely have experienced a Great Inflation like episode. Furthermore, we develop an estimation method to implement the adaptive learning hypothesis into DSGE modelling, thus extending the standard estimation toolbox.

The governance of major metropolitan areas is often associated with a "fragmented" and "uncoordinated" multi-government apparatus, frequently sculpted from years of particularistic ad hoc administrative reforms. This image of dysfunctional structure gains high salience when the metropolitan context is accentuated by complexity and fluidity, especially where intense paradoxical forces of economic development and ecological sustainability are present. The most visible "solutions" for such a state often come from bureaucrats seeking to "streamline" government according to norms of standardization and hierarchy. But, calls for reform may also come from scholars of polycentric government, who see the problem as a misalignment of administrative structure with the metropolitan context. This article adopts the latter, less-appreciated perspective that argues such dysfunctions in a metropolitan multi-government network are essentially problems of adaptive organizational design. Different than the bureaucratic model, treatises on new public management or group-behavior theory, it emphasizes the contextual nature of public administration by employing the holistic framework of "organizational systems." It illustrates the logic by introducing a toolbox for multi-government design that speaks to the adaptive qualities of government networks in whole metropolitan areas. Its purpose is to reinvigorate this holistic approach in thinking about the way we look at multi-government networks in major metropolitan areas.

Interventional digital media applications such as robotic surgery, remote-controlled vehicles or wearable tracking devices pose a challenge to media research methodologically as well as conceptually. How do we go about analyzing operational media, where human and non-human agencies intertwine in seemingly inscrutable ways? This article introduces the method of o perational analysis to systematically observe and critically analyze such situated, interventional and multilayered entanglements. Against the background of ongoing efforts to develop operational models for understanding digital media, the method of operational analysis conceptually ascribes to media technologies a real efficacy by approaching them as adaptive mediators. As an operational middle-range approach, it allows to integrate theoretical discussions with considerations of the situatedness, directedness, and task-orientation of operational media. The article presents an analytical toolbox for observing and analyzing digital media operations while simultaneously testing it on a particular application in robotic radiosurgery.

Cover -- Half Title -- Series -- Title -- Copyright -- Contents -- List of Figures -- List of Tables -- 1 An Introduction to Subversive Legal Education -- 2 A Visceral View of Subversion in Legal Education: Teaching and Research in Unusual Domains as a Methodology -- Part I Subverting the Legal Educator's Toolbox -- 3 Antithesis as Subversive Legal Education: Learning Justice Through Injustice in the Artwork of Sandro Botticelli -- 4 Subversion and Perspectivism in Teaching Property Law -- 5 Valuing our Differences: For the Sake of Adaptive Law Schools -- 6 Re-Thinking Assessment in Law -- Part II Subverting the Higher Education Sector -- 7 Can Law Schools Provide Students With a Subversive Legal Education in an Online Learning Environment? -- 8 Hacking the Priestleys -- Part III Subverting Through Courses -- 9 Value and Values in Higher Education: Some Reflections From the UK on the Subversive Dimensions of Historical Approaches in the Study of Law -- 10 Education for Citizenship and Social Justice: Students as Co-Creators -- 11 Unlearning Real Property Law -- 12 The Place of Politics in Teaching International Law -- Part IV Subverting Pathways to the Profession -- 13 Challenging BigLaw: Questioning the Dominant Discourse in Law Student Employment Aspirations -- Index.

As climate change and urban development are closely interlinked and often interact negatively, this edited volume takes Ho Chi Minh City (HCMC), Vietnamℓ́ℓs first mega-urban region as a case study to analyse its vulnerability to climate change and to suggest measures towards a more sustainable urban development. The book offers an overview on land use planning regarding the aspects of urban flooding, urban climate, urban energy and urban mobility as well as spatial views from the angle of urban planning such as the metropolitan level, the city, the neighbourhood and building level. It shows that to a significant degree, measures dealing with climate change can be taken from the toolbox of sustainable urban development and reflects how institutional structures need to change to enhance chances for implementation given socio-cultural and economic constraints. This is merged and integrated into a holistic perspective of planning recommendations, supporting the municipal government to increase its adaptive capacity. The authors are members of a German government funded research project on how to support HCMCℓ́ℓs municipal government to adapt to risks related to climate change.

The forward search (FS) is a general method of robust data fitting that moves smoothly from very robust to maximum likelihood estimation. The regression procedures are included in the MATLAB toolbox FSDA. The work on a SAS version of the FS originates from the need for the analysis of large datasets expressed by law enforcement services operating in the European Union that use our SAS software for detecting data anomalies that may point to fraudulent customs returns. Specific to our SAS implementation, the fsdaSAS package, we describe the approximation used to provide fast analyses of large datasets using an FS which progresses through the inclusion of batches of observations, rather than progressing one observation at a time. We do, however, test for outliers one observation at a time. We demonstrate that our SAS implementation becomes appreciably faster than the MATLAB version as the sample size increases and is also able to analyse larger datasets. The series of fits provided by the FS leads to the adaptive data-dependent choice of maximally efficient robust estimates. This also allows the monitoring of residuals and parameter estimates for fits of differing robustness levels. We mention that our fsdaSAS also applies the idea of monitoring to several robust estimators for regression for a range of values of breakdown point or nominal efficiency, leading to adaptive values for these parameters. We have also provided a variety of plots linked through brushing. Further programmed analyses include the robust transformations of the response in regression. Our package also provides the SAS community with methods of monitoring robust estimators for multivariate data, including multivariate data transformations.