The True Destination of EGO is Multi-local Optimization

Efficient global optimization is a popular algorithm for the optimization of expensive multimodal black-box functions. One important reason for its popularity is its theoretical foundation of global convergence. However, as the budgets in expensive optimization are very small, the asymptotic properties only play a minor role and the algorithm sometimes comes off badly in experimental comparisons. Many alternative variants have therefore been proposed over the years. In this work, we show experimentally that the algorithm instead has its strength in a setting where multiple optima are to be identified

The Too-Much-of-a-Good-Thing Effect in Management

A growing body of empirical evidence in the management literature suggests that antecedent variables widely accepted as leading to desirable consequences actually lead to negative outcomes. These increasingly pervasive and often countertheoretical findings permeate levels of analysis (i.e., from micro to macro) and management subfields (e.g., organizational behavior, strategic management). Although seemingly unrelated, the authors contend that this body of empirical research can be accounted for by a meta-theoretical principle they call the too-much-of-a-good-thing effect (TMGT effect). The authors posit that, due to the TMGT effect, all seemingly monotonic positive relations reach context-specific inflection points after which the relations turn asymptotic and often negative, resulting in an overall pattern of curvilinearity. They illustrate how the TMGT effect provides a meta-theoretical explanation for a host of seemingly puzzling results in key areas of organizational behavior (e.g., leadership, personality), human resource management (e.g., job design, personnel selection), entrepreneurship (e.g., new venture planning, firm growth rate), and strategic management (e.g., diversification, organizational slack). Finally, the authors discuss implications of the TMGT effect for theory development, theory testing, and management practice

The Grip of History and the Scope for Novelty: Some Results and Open Questions on Path Dependence in Economic Processes

-Path dependence, irreversibility, increasing returns, learning, lock-in.

Stock Portfolio Prediction by Multi-Target Decision Support

Investing in the stock market is a complex process due to its high volatility caused by factors as exchange rates, political events, inflation and the market history. To support investor's decisions, the prediction of future stock price and economic metrics is valuable. With the hypothesis that there is a relation among investment performance indicators,  the goal of this paper was exploring multi-target regression (MTR) methods to estimate 6 different indicators and finding out the method that would best suit in an automated prediction tool for decision support regarding predictive performance. The experiments were based on 4 datasets, corresponding to 4 different time periods, composed of 63 combinations of weights of stock-picking concepts each, simulated in the US stock market. We compared traditional machine learning approaches with seven state-of-the-art MTR solutions: Stacked Single Target, Ensemble of Regressor Chains, Deep Structure  for Tracking Asynchronous Regressor Stacking,   Deep  Regressor Stacking, Multi-output Tree Chaining,  Multi-target Augment Stacking  and Multi-output Random Forest (MORF). With the exception of MORF, traditional approaches and the MTR methods were evaluated with Extreme Gradient Boosting, Random Forest and Support Vector Machine regressors. By means of extensive experimental evaluation, our results showed that the most recent MTR solutions can achieve suitable predictive performance, improving all the scenarios (14.70% in the best one, considering all target variables and periods). In this sense, MTR is a proper strategy for building stock market decision support system based on prediction models

Agent-based learning for pattern matching in high-frequency trade data

A dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, 2017Previousresearchofsequentialinvestmentstrategiesforportfolioselectionhaveshownthatthereare strategies that exist that can beat the best stock in the market. In this dissertation, an algorithm is presented that uses a nearest neighbour approach similar to the one used by Gy¨orfi et al [20, 21, 22]. Theapproachishoweverextendedtoincludezero-costportfoliosandusesaquadraticapproximation, instead of an optimisation step, to determine how capital should be allocated in the portfolio based on the neighbours that have been found. A portfolio that results in an increase in the investor’s capitalandcomparesfavourablytocertainbenchmarks,suchasthebeststock,indicatesthatthereare patternsinthetimeseriesdata. Otherfeaturesofthealgorithmpresentedistoallowforthedatatobe clustered by a selection of stocks or partitioned based on time. The algorithm is tested on synthetic datasetsthatdepictdifferentmarkettypesandisshowntoaccuratelydeterminetrendsinthedata. The algorithm is then tested on real data from the New York Stock Exchange (NYSE) and data from the JohannesburgStockExchange(JSE).Theresultsofthealgorithmfromtherealdatasetsarecompared to implemented versions of past strategies from the literature and compares favourably.XL201

Adaptive social learning

This paper investigates the learning foundations of economic models of social learning. We pursue the prevalent idea in economics that rational play is the outcome of a dynamic process of adaptation. Our learning approach offers us the possibility to clarify when and why the prevalent rational (equilibrium) view of social learning is likely to capture observed regularities in the field. In particular it enables us to address the issue of individual and interactive knowledge. We argue that knowledge about the private belief distribution is unlikely to be shared in most social learning contexts. Absent this mutual knowledge, we show that the long-run outcome of the adaptive process favors non-Bayesian rational play.social Learning ; informational herding ; adaptation ; analogies ; non-Bayesian updating