Needs of Head and Neck Cancer Patients and Stakeholders During Rehabilitation

The foreseen growth of Head and Neck cancer (HNC) incidents will require future rehabilitation services to meet the needs of a wider population. This study reports the empirical findings of a case study conducted at a cancer rehabilitation center in Copenhagen, aiming to elicit the needs of HNC patients, informal caregivers and healthcare professionals (HCPs). Our results point out that patients and stakeholders' needs are interrelated, as they faced common challenges pertinent to provision and distribution of information. This study, though preliminary, underlines the importance of inclusion of all actors in the design of future interventions

Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric

Research during the last several decades indicates the failure of existing nutritional labels to substantially improve the healthfulness of consumers\u27 food/beverage choices. The present study aims to fill this void by developing a nutrition metric that is more comprehensible to the average shopper. The healthfulness ratings of 205 sample foods/beverages by leading nutrition experts formed the basis for a linear regression that places weights on 12 nutritional components (ie, total fat, saturated fat, cholesterol, sodium, total carbohydrate, dietary fiber, sugars, protein, vitamin A, vitamin C, calcium, and iron) to predict the average healthfulness rating that experts would give to any food/beverage. Major benefits of the model include its basis in expert judgment, its straightforward application, the flexibility of transforming its output ratings to any linear scale, and its ease of interpretation. This metric serves the purpose of distilling expert knowledge into a form usable by consumers so that they are empowered to make more healthful decisions

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Modeling Social Information in Conflict Situations through Instance-Based Learning Theory

Abstract: Behavior in conflict situations can be influenced by the social information that individuals have about their opponents. This paper tests whether an existent Instance-based Learning (IBL) model, built using the Instance-based Learning Theory (IBLT) to explain behavior in a single-person binary-choice task (BCT), can predict behavior in a two-player iterated prisoner’s dilemma (IPD) game. The same IBL model is generalized to two conditions in the IPD: Social, where individuals have information about their opponents and their choices; and Non-social, where individuals and opponents lack this information. We expect the single-person IBL model to predict behavior in the Non-social condition better than in the Social condition. However, due to the structural differences between BCT and IPD, we also expect only moderately good model predictions in the Non-social condition. Our results confirm these expectations. These findings highlight the need for additional cognitive mechanisms to account for social information in conflict situations

Scaling up Instance-Based Learning Theory to Account for Social Interactions

Traditional economic theory often describes real-world social dilemmas as abstract games where an individual’s goal is to maximize economic benefit by cooperating or competing with others. Despite extensive empirical work, descriptive models of human behavior in social dilemmas are lacking in both cognitive realism and predictive power. This article addresses a central challenge arising from the success of modeling individuals making decisions from experience: our ability to scale these models up to explain social interactions. We propose that models based on the instance-based learning theory (IBLT) will help us to understand how conflictual social interactions are influenced by prior experiences of involved individuals and by information available to them during the course of interaction. We present mechanisms by which IBLT might capture the effects of social interaction with different levels of information without assuming predefined interaction strategies, but rather by assuming learning from experience.</p

Cognitive Aspects of Power in a Two-Level Game

The Intergroup Prisoner’s Dilemma with Intragroup Power Dynamics (IPD^2) is a new game paradigm for studying human behavior in conflict situations. IPD^2 adds the concept of intragroup power to an intergroup version of the standard Iterated Prisoner’s Dilemma game. We conducted an exploratory laboratory study in which individual human participants played the game against computer strategies of various complexities. We also developed a cognitive model of human decision making in this game. The model was run in place of the human participant under the same conditions as in the laboratory study. Results from the human study and the model simulations are presented and discussed, emphasizing the value of including intragroup power in game theoretic models of conflict

A Description-Experience Gap in Social Interactions: Information about Interdependence and Its Effects on Cooperation

In social interactions, decision makers are often unaware of their interdependence with others, precluding the realization of shared long-term benefits. In an experiment, pairs of participants played an Iterated Prisoner\u27s Dilemma under various conditions involving differing levels of interdependence information. Each pair was assigned to one of four conditions: \u27No-Info\u27 players saw their own actions and outcomes, but were not told that they interacted with another person; \u27Min-Info\u27 players knew they interacted with another person but still without seeing the other\u27s actions or outcomes; \u27Mid-Info\u27 players discovered the other\u27s actions and outcomes as they were revealed over time; and \u27Max-Info\u27 players were also shown a complete payoff matrix mapping actions to outcomes from the outset and throughout the game. With higher levels of interdependence information, we found increased individual cooperation and mutual cooperation, driven by increased reciprocating cooperation (in response to a counterpart\u27s cooperation). Furthermore, joint performance and satisfaction were higher for pairs with more information. We discuss how awareness of interdependence may encourage cooperative behavior in real-world interactions

A Game Paradigm to Study the Dynamics of Power

Game theory paradigms like the Prisoner’s Dilemma have provided an extremely successful avenue for mathematical and computational analysis of emergent dynamics in a wide array of social science situations. However, simple 2x2 games fail to capture more complex interactions involving multiple parties and changing payoffs. In this paper, we propose an elaboration of the Iterated Prisoner’s Dilemma paradigm to capture the dynamics of power between groups of competing and cooperating individuals. The new game, IPD^2, features two levels of competition and cooperation both within and between groups of individuals. We present results of simulations involving several simple agents that allow us to explore the dynamics of the game. Finally, we discuss the various social science phenomena that this game will allow us to study systematically through both the collection of human data and simulation of automated agents