The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections

This paper rectifies a design problem in the Santa Fe Artificial Stock Market Model. Due to a faulty mutation operator, the resulting bit distribution in the classifier system was systematically upwardly biased, thus suggesting increased levels of technical trading for smaller GA-invocation intervals. The corrected version partly supports the Marimon-Sargent-Hypothesis that adaptive classifier agents in an artificial stock market will always discover the homogeneous rational expectation equilibrium. While agents always find the correct solution of non-bit usage, analyzing the time series data still suggests the existence of two different regimes depending on learning speed. Finally, classifier systems and neural networks as data mining techniques in artificial stock markets are discussed.Asset Pricing; Learning; Financial Time Series; Genetic Algorithms; Classifier Systems; Agent-Based Simulation

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs

Monetary Policy Rules in a Non-Rational World: A Macroeconomic Experiment

I introduce a new learning-to-forecast experimental design, where subjects in a virtual New-Keynesian macroeconomy based on Woodford (2013) need to forecast individual instead of aggregate outcomes. This approach is motivated by the critique of Preston (2005) and Woodford (2013) that substituting arbitrary forms of expectations into the reduced-form New-Keynesian model (consisting of the “DIS” equation, the “Phillips curve” and the “Taylor” rule) is inconsistent with its microfoundations. Using this design, I analyze the impact of different interest rate rules on expectation formation and expectation-driven fluctuations. Even if the Taylor principle is fulfilled, instead of quickly converging to the REE, the experimental economy exhibits persistent purely expectation-driven fluctuations not necessarily around the REE. Only a particularly aggressive monetary authority achieves the elimination of these fluctuations and quick convergence to the REE. To explain the aggregate behavior in the experiment, I develop a “noisy” adaptive learning approach, introducing endogenous shocks into a simple adaptive learning model. However, I find that for some monetary policy regimes a reinforcement learning model, applied to different forecasting rules, provides a better fit to the data

An Examination of Project Based Learning at the Secondary Level: A Review of Literature

Project based learning (PBL) is a pedagogical approach designed to capture student interest by integrating a contemporary and relevant problem or issue with content standards. The expectation is that students who have not been successful under traditional teacher‐ centered/ lecture‐oriented instruction will be taken in by the opportunity to investigate a topic of personal interest using PBL. Moreover, some say that when constructivist learning (upon which PBL is based) is designed to reflect contemporary research methods and design protocols, students will be better prepared for the 21st century workplace (Apedoe, Reynolds, Ellefson & Schunn, 2008; Bell, 2010; Technology Assistance Program, 1998)

IMPACT: Customized Faculty Development for Learner-Centered Course Redesign

IMPACT at Purdue University works with instructors to redesign large-enrollment, foundational courses with the aim of engaging students more fully in their learning and creating a more student-centered environment, with the expectation that this will improve student success. IMPACT faculty are guided through a semester-long course of FLC (Faculty Learning Community) sessions based on IMPACT\u27s design model. Faculty also work with a small support team that provides guidance and expertise in the areas of educational technology, instructional design, information literacy, and learning assessment. Year-three program and course assessment measures and results will be discussed. Outcomes: Discuss IMPACT\u27s program of support for faculty in creating student-centered, active learning * List the program and course assessments used by the IMPACT program * Formulate useful takeaways for your institution\u27s faculty development program

A Context of the Education Management Based on Multicultural Education for Ethnic Students in Higher Education Institutions of Northern Thailand

This research aimed to 1) explore current conditions and issues on education management based on multicultural education for ethnic students in higher education institutions in northern Thailand, and 2) consider their expectation of the education management drawn upon the same. A sample group was 356 ethnic students, at their bachelor degree level, in the second semester for the year 2014, in 11 higher education institutions. The data collection instrument was a questionnaire on education management, based on multicultural education, with 3 aspects. The first aspect was institution administration: policy; organizational culture; attitudes, beliefs, and actions of staff; community participation; and language/native language of the institution. The second was curriculum and teaching and learning management: curriculum design and course of study, and institution’s learning pattern. And the third was students’ quality development: student counseling service and student assistance program. Statistics employed for analysis were frequency, percentage, arithmetic mean, and standard deviation. The findings demonstrated that the ethnic students have, with a statistical significance, the expectation of the education management in all aspects. Further, a new body of knowledge derived was that the education management clearly impacted their learning achievement. In order to enhance efficiency of education management and to elevate learning achievement of ethnic students, therefore, these institutions should design and develop a model of education management based on multicultural education, which is necessarily suitable for these students. Given this, however, such a model must be corresponding to and in accordance with the direction of the institution development strategy

A General Theory of Sample Complexity for Multi-Item Profit Maximization

The design of profit-maximizing multi-item mechanisms is a notoriously challenging problem with tremendous real-world impact. The mechanism designer's goal is to field a mechanism with high expected profit on the distribution over buyers' values. Unfortunately, if the set of mechanisms he optimizes over is complex, a mechanism may have high empirical profit over a small set of samples but low expected profit. This raises the question, how many samples are sufficient to ensure that the empirically optimal mechanism is nearly optimal in expectation? We uncover structure shared by a myriad of pricing, auction, and lottery mechanisms that allows us to prove strong sample complexity bounds: for any set of buyers' values, profit is a piecewise linear function of the mechanism's parameters. We prove new bounds for mechanism classes not yet studied in the sample-based mechanism design literature and match or improve over the best known guarantees for many classes. The profit functions we study are significantly different from well-understood functions in machine learning, so our analysis requires a sharp understanding of the interplay between mechanism parameters and buyer values. We strengthen our main results with data-dependent bounds when the distribution over buyers' values is "well-behaved." Finally, we investigate a fundamental tradeoff in sample-based mechanism design: complex mechanisms often have higher profit than simple mechanisms, but more samples are required to ensure that empirical and expected profit are close. We provide techniques for optimizing this tradeoff

Changing the mindset:An innovative work-based learning programme within an English higher education institution

This paper aims to explore and critically analyse the perceptions and experiences of academics in relation to the design and delivery of an innovative Work Based Learning (WBL) programme within an English higher education institution (HEI). These perceptions were gathered through semi-structured interviews and subjected to discourse analysis. Consequently, the key themes which have emerged are: (i) the intensity of the learning experience, (ii) the tensions and pressures amongst academics delivering the programme, for example an expectation that academics „get it right first time‟, and (iii) learning support for students. The paper concludes with recommendations for future policy and research