Try, try again: Lessons learned from success and failure in participatory modeling

Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors' cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three categories of reflection: a) lessons learned about participatory modelers; b) lessons learned about the context of collaboration; and c) lessons learned about the PM process. First, successful PM teams encompass a variety of skills beyond modeling expertise. Skills include: effective relationship-building, openness to learn from local experts, awareness of personal motivations and biases, and ability to translate discussions into models and to assess success. Second, the context for collaboration necessitates a culturally appropriate process for knowledge generation and use, for involvement of community co-leads, and for understanding group power dynamics that might influence how people from different backgrounds interact. Finally, knowing when to use PM and when not to, managing expectations, and effectively and equitably addressing conflicts is essential. Managing the participation process in PM is as important as managing the model building process. We recommend that PM teams consider what skills are present within a team, while ensuring inclusive creative space for collaborative exploration and learning supported by simple yet relevant models. With a realistic view of what it entails, PM can be a powerful approach that builds collective knowledge and social capital, thus helping communities to take charge of their future and address complex social and environmental problems

A phenomenological approach to normal form modeling: a case study in laser induced nematodynamics

An experimental setting for the polarimetric study of optically induced dynamical behavior in nematic liquid crystal films has allowed to identify most notably some behavior which was recognized as gluing bifurcations leading to chaos. This analysis of the data used a comparison with a model for the transition to chaos via gluing bifurcations in optically excited nematic liquid crystals previously proposed by G. Demeter and L. Kramer. The model of these last authors, proposed about twenty years before, does not have the central symmetry which one would expect for minimal dimensional models for chaos in nematics in view of the time series. What we show here is that the simplest truncated normal forms for gluing, with the appropriate symmetry and minimal dimension, do exhibit time signals that are embarrassingly similar to the ones found using the above mentioned experimental settings. The gluing bifurcation scenario itself is only visible in limited parameter ranges and substantial aspect of the chaos that can be observed is due to other factors. First, out of the immediate neighborhood of the homoclinic curve, nonlinearity can produce expansion leading to chaos when combined with the recurrence induced by the homoclinic behavior. Also, pairs of symmetric homoclinic orbits create extreme sensitivity to noise, so that when the noiseless approach contains a rich behavior, minute noise can transform the complex damping into sustained chaos. Leonid Shil'nikov taught us that combining global considerations and local spectral analysis near critical points is crucial to understand the phenomenology associated to homoclinic bifurcations. Here this helps us construct a phenomenological approach to modeling experiments in nonlinear dissipative contexts.Comment: 25 pages, 9 figure

Integrating Institutional and Neoclassical Economics Using Game Theory

We contend that economics is in need of a new paradigm. When taught in the traditional neoclassical construct, economics is not appealing to many students, particularly those taking economics to satisfy general education requirements. This, in turn, may explain why economic literacy in the U.S. is trending downward. Our approach uses game theory to teach economics by juxtaposing neoclassical and institutional economic paradigms so that students not only better understand the neoclassical model, but also are exposed to alternative approaches in economics. To better understand neoclassical models, we incorporate three institutional criticisms of neoclassical economics: (1) the assumption that all individuals are perfectly rational, (2) the propensity to create theories using static models that take as given (and usually ignore) important, exogenous factors influencing decision-making processes, and (3) an emphasis on modeling techniques and less of an emphasis on what is actually being modeled and how it is being modeled. We also integrate institutional and neoclassical economics by illustrating how history, culture, and emotion interact with the traditional neoclassical principles to inform economic decision-making via simple (evolutionary) games that can be used in the classroom

Integrating Cognitive Learning Strategies into Physics Instruction : Developing students’ approaches to physics and learning

Introductory physics courses are obligatory for many disciplines outside of physics. As experienced by many students, they are notoriously difficult, often with high failure rates. Many students, whether they passed or failed a physics course, fail to acquire the required conceptual knowledge and skill to become able to model complex situations with physics principles. In some cases, this can be attributed to a lack of study time; in many cases, it can be attributed to inefficient learning strategies. The aim of this thesis was to find ways to create self-regulated physics students who use effective learning strategies, achieve a deep understanding of physics principles, and, ultimately, become able to solve conceptually challenging physics problems through the use of physics modeling. In this research project, we have identified and tried to fill some of the gaps in students’ knowledge that hinder them from becoming able to practice physics modeling. Research within cognitive science, educational psychology, and physics education has informed us about the structure of the knowledge students fail to learn. We matched proven, effective learning strategies to each aspect of this cognitive knowledge structure and we developed tools for scaffolding the process. In the first phase of the first paper, we investigated students’ memory for physics principles and basic facts shortly before the exam and experimentally tested the efficacy of retrieval practice of a novel hierarchical principle structure for improving their declarative memory. The results showed that many of the control group students had a severe lack in their memory for basic facts and principles and that seventy minutes of retrieval practice resulted in large gains for the experimental group. In the second phase, we implemented structured retrieval practice in lectures throughout the semester. The multiple regression model indicated that retrieval practice improved students’ results on the final exam, especially for the weaker students. In the second paper, we quasi-experimentally (study 1) and experimentally (study 2) tested the effects of doing retrieval practice before self-explanation on posttest problem-solving and conceptual scores. In sum, results indicated a medium-sized effect of doing retrieval practice on the problem-solving score. The results were inconclusive for the score on conceptual tests. We also investigated the knowledge students should seek to acquire when self-explaining worked examples in physics. The results from the two studies indicated that when explaining the physics model, students should seek to explicate the principles and their conditions of application, how the principle is set up, and how the physics model can lead to the goal of the problem; and when explaining the mathematical procedures, students should seek to explicate what is done in the particular procedural action, the goal of that action, and the conditions for its application. In the third paper, we built on the results and experiences from the first two papers and tried to integrate three learning strategies and three scaffolding tools into an introductory mechanics course. The three learning strategies were elaborative encoding for acquiring associative links within and between physics principles; retrieval practice for building strong memories of physics principles; and self-explanations for building effective declarative rules for problem-solving. The three tools were: A set of elaborative encoding-questions as a scaffold for elaborative encoding; the Hierarchical Principle Structure for Mechanics, which together with retrieval practice was meant for scaffolding students’ construction of a meaningful and hierarchical cognitive knowledge structure; and a problem-solution structure with emphasis on physics modeling for scaffolding self-explanation and for developing knowledge and skills in physics modeling. Using thematic analysis, we found that the two main encoding strategies—elaborative encoding and self-explanation—require substantial work for overcoming the existing barriers to student adoption and achieving effective implementation. We had more success with the integration of retrieval practice, the hierarchical principle structure, and the practice of physics modeling during problem-solving. The paper provided multiple suggestions for how to overcome barriers and better integrate these learning strategies and tools into the structure of physics courses. Together, these three papers contribute to the physics education research literature with increased knowledge of how we can support students’ conceptual learning, from simple cognitive learning processes like elaborative encoding to the complex practice of physics modeling; with new tools for scaffolding students’ conceptual learning in introductory physics, especially the Hierarchical Principle Structure for Mechanics and the problem-solution structure; and with insights into barriers to students’ adoption of effective learning strategies.Doktorgradsavhandlin

What drives mobile in-app purchases? An extended framework of in-app purchase and monetary effort

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligencePast research has focused on mobile app use intention and acceptance but did not provide insights on the factors influencing in-app purchase and monetary effort. This study contributes to filling this gap by analyzing how relevant personality-based variables such as stickiness and innovativeness influence in-app purchase and monetary effort. We extend the affect–behavior–cognition (ABC) model of attitudes by developing a framework that evaluates the relevance of not only attitude but also stickiness, satisfaction, social identification, and innovativeness on mobile in-app purchase intention and monetary effort. We develop a study with 303 European consumers about mobile apps, using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that attitude, satisfaction, innovativeness, and stickiness help to explain purchase intention and monetary effort of mobile in-app purchases. In addition, the findings suggest that attitude and satisfaction are, respectively, important moderators of stickiness and in-app purchase intention on in-app monetary effort. The findings have implications for companies on what factors to consider when developing a mobile app for monetization purposes

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

The control of frequency of a conditional oscillator simultaneously subjected to multiple oscillatory inputs

A conditional oscillator is one that requires input to oscillate. An example of such is the gastric mill network of the stomatogastric ganglion of the crab Cancer borealis which requires modulatory input from outside the stomatogastric ganglion and fast input from the pyloric network of the animal in order to become active. This dissertation studies how the frequency of the gastric mill network is determined when it is simultaneously subjected to two different rhythmic inputs whose timing may be mismatched. We derive a mathematical model of the gastric mill network and deduce that the difference in timing between the pyloric and modulatory inputs is crucial in determining what effect it will have on the frequency of the gastric mill network. Over a certain range of the time mismatch, the pyloric input plays no role in determining the network frequency, while in another range of the time mismatch, both inputs work together to determine the frequency. The existence and stability of periodic solutions to the modeling set of equations are obtained analytically using geometric singular perturbation theory and an analytic approximation of the frequency is obtained. The results are validated through numerical simulations of the model and are shown to extend to a detailed Hodgkin-Huxley type compartmental model of the gastric mill network. Comparisons to experiments are also presented

Learning and Games

Part of the Volume on the Ecology of Games: Connecting Youth, Games, and Learning In this chapter, I argue that good video games recruit good learning and that a game's design is inherently connected to designing good learning for players. I start with a perspective on learning now common in the Learning Sciences that argues that people primarily think and learn through experiences they have had, not through abstract calculations and generalizations. People store these experiences in memory -- and human long-term memory is now viewed as nearly limitless -- and use them to run simulations in their minds to prepare for problem solving in new situations. These simulations help them to form hypotheses about how to proceed in the new situation based on past experiences. The chapter also discusses the conditions experience must meet if it is to be optimal for learning and shows how good video games can deliver such optimal learning experiences. Some of the issues covered include: identity and learning; models and model-based thinking; the control of avatars and "empathy for a complex system"; distributed intelligence and cross-functional teams for learning; motivation, and ownership; emotion in learning; and situated meaning, that is, the ways in which games represent verbal meaning through images, actions, and dialogue, not just other words and definitions