Economics of sociality

Ziel der vorliegenden Arbeit ist es, die grundlegenden Mechanismen kooperativen sowie kompetitiven Verhaltens zu erläutern. Da gerade in den letzten Jahren vermehrt versucht wurde, Ergebnisse verhaltensökonomischer Studien auch anhand evolutionsbiologischer Hypothesen über das Verhalten von Menschen zu erklären, werden in einem ersten Schritt die Grundannahmen der evolutionären Biologie und Anthropologie über das Entstehen und die Entwicklung kooperativer Verhaltensmuster aufgeführt. Da die Organisationsstruktur der Gruppe substanziellen Ein&uss auf die adaptierten sozialen und kompetitiven Verhaltensstrategien von Individuen nimmt, wird des weiteren ein besonderer Fokus auf die Evolution hierarchischer Strukturen, sowohl beim Menschen, als auch bei nicht menschlichen Primaten, gelegt. Die folgende Zusammenfassung der wichtigsten Ergebnisse verhaltensökonomischer Studien über das Entstehen und die Charakteristika sozialer Präferenzen komplettiert den Stand der Forschung und erlaubt eine kritische Evaluation der heutigen Sichtweise kooperativen Verhaltens. Hierdurch ist es möglich einige strukturelle Fehler der experimentellen Forschung, sowie mögliche Fehlinterpretationen kooperativer Verhaltensstrategien aufzudecken. Es zeigt sich, dass die interdisziplinäre Orientierung der verhaltensökonomischen Forschung über soziale Präferenzen diverse Probleme mit sich bringt. Ein kurzer Exkurs in die aktuellen Fragestellungen der Forschung zum sozio-ökonomischen Status, soll auf der einen Seite noch einmal die Wichtigkeit der sozialen Struktur als möglichen Steuerungsmechanismus sozialer Präferenzen herausstellen. Auf der anderen Seite soll hierdurch ein interdisziplinäres Forschungsprojekt, Occupational Ethology (Wallner et al., 2008 ) vorgestellt werden. Ziel dieses Projektes ist es, genau an der Schnittstelle zwischen Kooperation und Wettbewerb in der Hierarchie zu forschen und soziale Strategien und deren Folgen zu erklären.This master thesis aims at providing a more complete understanding of what are the underlying mechanisms of cooperation and competition and the behavioral transition Schritt die Grundannahmen der evolutionären Biologie und Anthropologie über das Entstehen und die Entwicklung kooperativer Verhaltensmuster aufgeführt. Da die Organisationsstruktur der Gruppe substanziellen Ein&uss auf die adaptierten sozialen und kompetitiven Verhaltensstrategien von Individuen nimmt, wird des weiteren ein besonderer Fokus auf die Evolution hierarchischer Strukturen, sowohl beim Menschen, als auch bei nicht menschlichen Primaten, gelegt. Die folgende Zusammenfassung der wichtigsten Ergebnisse verhaltensökonomischer Studien über das Entstehen und die Charakteristika sozialer Präferenzen komplettiert den Stand der Forschung und erlaubt eine kritische Evaluation der heutigen Sichtweise kooperativen Verhaltens. Hierdurch ist es möglich einige strukturelle Fehler der experimentellen Forschung, sowie mögliche Fehlinterpretationen kooperativer Verhaltensstrategien aufzudecken. Es zeigt sich, dass die interdisziplinäre Orientierung der verhaltensökonomischen Forschung über soziale Präferenzen diverse Probleme mit sich bringt. Ein kurzer Exkurs in die aktuellen Fragestellungen der Forschung zum sozio-ökonomischen Status, soll auf der einen Seite noch einmal die Wichtigkeit der sozialen Struktur als möglichen Steuerungsmechanismus sozialer Präferenzen herausstellen. Auf der anderen Seite soll hierdurch ein interdisziplinäres Forschungsprojekt, Occupational Ethology (Wallner et al., 2008 ) vorgestellt werden. Ziel dieses Projektes ist es, genau an der Schnittstelle zwischen Kooperation und Wettbewerb in der Hierarchie zu forschen und soziale Strategien und deren Folgen zu erklären

Stability Properties of the Adaptive Horizon Multi-Stage MPC

This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical stability (ISpS). The proposed algorithm employs parametric nonlinear programming (NLP) sensitivity and terminal ingredients to determine the minimum stabilizing prediction horizon for all the scenarios considered in the subsequent iterations of the multi-stage MPC. This technique notably decreases the computational cost in nonlinear model-predictive control systems with uncertainty, as they involve solving large and complex optimization problems. The efficacy of the controller is illustrated using three numerical examples that illustrate a reduction in computational delay in multi-stage MPC.Comment: Accepted for publication in Elsevier's Journal of Process Contro

Self-optimizing control – A survey

Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research

Moving speeches: Dominance, trustworthiness and competence in body motion

AbstractPeople read dominance, trustworthiness and competence into the faces of politicians but do they also perceive such social qualities in other nonverbal cues? We transferred the body movements of politicians giving a speech onto animated stick-figures and presented these stimuli to participants in a rating-experiment. Analyses revealed single body postures of maximal expansiveness as strong predictors of perceived dominance. Also, stick-figures producing expansive movements as well as a great number of movements throughout the encoded sequences were judged high on dominance and low on trustworthiness. In a second step we divided our sample into speakers from the opposition parties and speakers that were part of the government as well as into male and female speakers. Male speakers from the opposition were rated higher on dominance but lower on trustworthiness than speakers from all other groups. In conclusion, people use simple cues to make equally simple social categorizations. Moreover, the party status of male politicians seems to become visible in their body motion

GivEn -- Shape Optimization for Gas Turbines in Volatile Energy Networks

This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithms. Given the many start and shut-down processes of gas power plants in volatile energy grids, besides optimizing gas turbine geometries for efficiency, the durability understood as minimization of the probability of failure is a design objective of increasing importance. We also describe the underlying coupling structure of the multiphysical simulations and use modern, gradient based multicriteria optimization procedures to enhance the exploration of Pareto-optimal solutions

Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study

To operate process engineering systems in a safe and reliable manner, predictive models are often used in decision making. In many cases, these are mechanistic first principles models which aim to accurately describe the process. In practice, the parameters of these models need to be tuned to the process conditions at hand. If the conditions change, which is common in practice, the model becomes inaccurate and needs to be re-tuned. In this paper, we propose a hybrid modeling machine learning framework that allows tuning first principles models to process conditions using two different types of Bayesian Neural Networks. Our approach not only estimates the expected values of the first principles model parameters but also quantifies the uncertainty of these estimates. Such an approach of hybrid machine learning modeling is not yet well described in the literature, so we believe this paper will provide an additional angle at which hybrid machine learning modeling of physical systems can be considered. As an example, we choose a multiphase pipe flow process for which we constructed a three-phase steady state model based on the drift-flux approach which can be used for modeling of pipe and well flow behavior in oil and gas production systems with or without the neural network tuning. In the simulation results, we show how uncertainty estimates of the resulting hybrid models can be used to make better operation decisions.Comment: 20 pages, 10 figures, not published in any journa

Simplified First-Principles Model of a Compact Flotation Unit for Use in Optimization and Control

In this paper, we develop a simplified control-oriented model of a compact flotation unit (CFU), which removes residual oil from produced water in oil and gas production systems. CFU is a class of separators that exploits the synergy between separation effects of a swirling flow and the effect of flotation, in which small gas bubbles attach to the oil droplets and float to the top of the separator, where they are removed. The purified water flows downward, and is removed from the bottom. Our CFU model consists of a simplified initial swirl separation part and a flotation part, in which populations of oil droplets, gas bubbles without oil droplet, and gas bubbles with oil droplet attached are tracked spatially. After analyzing the model, we use it as a basis for designing a control structure that operates the separation system optimally

Modeling and control of an inline deoiling hydrocyclone *

In subsea oil and gas production and processing, automatic control of operation is of significant importance. Typically, processing in subsea fields involves separation of hydrocarbons from water and rejection of water in an environmentally friendly way. Separators such as deoiling hydrocyclones help achieve these objectives. However, control strategy for hydrocyclones is not yet well established in the literature due to a lack of control oriented models for hydrocyclone. In this work we present a model for hydrocyclone based on mass balance equations. Subsequently, we propose a PI controller for controlling the water quality

Fast Sensitivity-Based Nonlinear Economic Model Predictive Control with Degenerate NLP

We present a fast sensitivity-based nonlinear model predictive control (NMPC) algorithm, that can handle non-unique multipliers in the discretized dynamic optimization problem. Non-unique multipliers may arise, for example when path constraints are active for longer periods of the prediction horizon. This is a common situation in economic model predictive control. In such cases, the optimal nonlinear programming (NLP) solution often satisfies the Mangasarian-Fromovitz constraint qualification (MFCQ), which implies non-unique, but bounded multipliers. Consequently, any sensitivity-based fast NMPC scheme must allow for discontinuous jumps in the multipliers. In this paper, we apply a sensitivity-based path-following algorithm that allows multiplier jumps within the advance-step NMPC (asNMPC) framework. The path-following method consists of a corrector and a predictor step, which are computed by solving a system of linear equations, and a quadratic programming problem, respectively, and a multiplier jump step determined by the solution of a linear program. We demonstrate the proposed method on an economic NMPC case study with a CSTR.publishedVersion2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Multiple shooting for training neural differential equations on time series

Neural differential equations have recently emerged as a flexible data-driven/hybrid approach to model time-series data. This work experimentally demonstrates that if the data contains oscillations, then standard fitting of a neural differential equation may result in a “flattened out” trajectory that fails to describe the data. We then introduce the multiple shooting method and present successful demonstrations of this method for the fitting of a neural differential equation to two datasets (synthetic and experimental) that the standard approach fails to fit. Constraints introduced by multiple shooting can be satisfied using a penalty or augmented Lagrangian method