Designing IS service strategy: an information acceleration approach

Information technology-based innovation involves considerable risk that requires insight and foresight. Yet, our understanding of how managers develop the insight to support new breakthrough applications is limited and remains obscured by high levels of technical and market uncertainty. This paper applies a new experimental method based on “discrete choice analysis” and “information acceleration” to directly examine how decisions are made in a way that is behaviourally sound. The method is highly applicable to information systems researchers because it provides relative importance measures on a common scale, greater control over alternate explanations and stronger evidence of causality. The practical implications are that information acceleration reduces the levels of uncertainty and generates a more accurate rationale for IS service strategy decisions

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search

Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments

A Nation Deceived: How Schools Hold Back America's Brightest Students, Volume II

Provides a comprehensive review of research on the academic acceleration of gifted students

Maximizing Competency Education and Blended Learning: Insights from Experts

In May 2014, CompetencyWorks brought together twenty-three technical assistance providers to examine their catalytic role in implementing next generation learning models, share each other's knowledge and expertise about blended learning and competency education, and discuss next steps to move the field forward with a focus on equity and quality. Our strategy maintains that by building the knowledge and networks of technical assistance providers, these groups can play an even more catalytic role in advancing the field. The objective of the convening was to help educate and level set the understanding of competency education and its design elements, as well as to build knowledge about using blended learning modalities within competency-based environments. This paper attempts to draw together the wide-ranging conversations from the convening to provide background knowledge for educators to understand what it will take to transform from traditional to personalized, competency-based systems that take full advantage of blended learning

Integration of Culturally Responsive Pedagogy in Reading Intervention

Pichel, A. (2020). Integration Of Culturally Sustaining Practices In Reading Intervention As the achievement gap persists in Minnesota between white students and students of color, many districts seek to accelerate achievement for students performing below grade-level expectations. Reading specialists diagnose and develop instructional plans to accelerate reading achievement. To accelerate reading achievement, teachers and reading interventionists must support culturally and linguistically diverse (CLD) students through culturally sustaining pedagogy (CSP). CSP builds an understanding of students’ cultural assets to inform instructional decisions which consequently reduces stress for students in the classroom allowing increased academic achievement. To support reading achievement, teachers make informed decisions by selecting effective reading strategies based on reading content knowledge and CSP. This project connected the diverse student population at my school site to reframe Pathways to Reading Excellence at School Sites (PRESS) created by the Minnesota Center for Reading Research, reading interventions with CSP strategies. Developing a variety of instructional options to support CLD students addresses the lack of CSP strategies missing from PRESS reading interventions. These missing strategies and other supplemental materials to support CSP in the reading intervention classroom were collected on a website. To equitably share resources and information for CSP in reading intervention, I created a website, http://sustainingteaching.org. The website is organized into three components: “Know”, “Teach”, and “Learn”. The components reflect a collection of original and sourced materials to supplement the provided materials from PRESS and integrate culturally sustaining pedagogy

A Virtual Laboratory for the Study of History and Cultural Dynamics

This article presents a Virtual Laboratory that enables the researcher to try hypothesis and confirm data analysis about different historical processes and cultural dynamics. This Virtual Cultural Laboratory (VCL) is developed using agent-based modeling technology. Individuals' tendencies and preferences as well as the behavior of cultural objects in the transformation of cultural information are taken into consideration. In addition, the effect of local interactions at different scales over time and space is visualized through the VCL interface. Information repositories, cultural items, borders, population size, individual' tendencies and other features are determined by the user. Finally, the researcher can also isolate specific factors whose effect on the global system might be of interest to the researcher. All the code can be found at http://projects.cultureplex.ca/Cultural Dynamics, Cultural Complexity, Multi-Agent Based Simulation, Netlogo, Virtual Laboratory