Virtuous opinion change in structured groups

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed

Virtuous opinion change in structured groups

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed

Structured groups make more accurate veracity judgements than individuals

Groups often make better judgements than individuals, and recent research suggests that this phenomenon extends to the deception detection domain. The present research investigated whether the influence of groups enhances the accuracy of judgements, and whether group size influences deception detection accuracy. Two-hundred fifty participants evaluated written statements with a pre-established detection accuracy rate of 60% in terms of veracity before viewing either the judgements and rationales of several other group members or a short summary of the written statement and revising or restating their own judgements accordingly. Participants' second responses were significantly more accurate than their first, suggesting a small positive effect of structured groups on deception detection accuracy. Group size did not have a significant effect on detection accuracy. The present work extends our understanding of the utility of group deception detection, suggesting that asynchronous, structured groups outperform individuals at detecting deception

Using the theoretical domains framework to explore primary health care practitioner’s perspectives and experiences of preconception physical activity guidance and promotion.

Preconception physical activity is one of the main predictors of continued engagement in physical activity during and after pregnancy and subsequently, improves the health of women and their child. In the UK, guidance states that Primary Care health Professionals (PCPs) should assess and discuss the lifestyle of preconception women, in routine appointments, in order to address potentially modifiable risk factors. However, knowledge and provision of this guidance in the UK is unknown. It is not clear if individuals actively seek preconception guidance from PCPs, what guidance they request, and whether PCPs have the knowledge and skills to provide this support in line with current guidelines. This research aimed to explore current practice and the perspectives of PCPs in delivering physical activity guidance to preconception patients. Fifteen semi-structured interviews were conducted with PCPs (GPs and community pharmacists) in the UK. Data was analysed using the Theoretical Domains Framework (TDF). Our findings showed patients did not frequently present solely for preconception physical activity guidance, but occasionally enquired when consulting about another issue. PCPs lacked motivation to implement physical activity guidance due to the perception that their advice would have no impact on behaviour change. There were a number of perceived opportunities to implement preconception physical activity guidance. These findings illustrate the need for consistent and specific preconception lifestyle and PA guidance for PCPs

Delphi with feedback of rationales: How large can a Delphi group be such that participants are not overloaded, de-motivated, or disengaged?

In this paper, we investigate the effect of Delphi group size and opinion diversity on group members’ information load as well as on their overall experience of the Delphi process - in terms of task involvement (enjoyment and interest) and in terms of group sway (the influence and helpfulness of others’ rationales). For Delphi applications involving the exchange of rationales between participants, we found no evidence that group sizes of up to 19 participants cause information overload or de-motivation and disengagement of participants

Improving the production and evaluation of structural models using a Delphi process

Bayes Nets (BNs) are extremely useful for causal and probabilistic modelling in many real-world applications, often built with information elicited from groups of domain experts. But their potential for reasoning and decision support has been limited by two major factors: the need for significant normative knowledge, and the lack of any validated methods or software supporting collaboration. Consequently, we have developed a web-based structured technique – Bayesian Argumentation via Delphi (BARD) – to enable groups of domain experts to receive minimal normative training and then collaborate effectively to produce high-quality BNs. BARD harnesses multiple perspectives on a problem, while minimising biases manifest in freely interacting groups, via a Delphi process: solutions are first produced individually, then shared, followed by an opportunity for individuals to revise their solutions. To test the hypothesis that BNs improve due to Delphi, we conducted an experiment whereby individuals with a little BN training and practice produced structural models using BARD for two Bayesian reasoning problems. Participants then received 6 other structural models for each problem, rated their quality on a 7-point scale, and revised their own models if they wished. Both top-rated and revised models were on average significantly better quality (scored against a gold-standard) than the initial models, with large and medium effect sizes. We conclude that Delphi – and BARD – improves the quality of BNs produced by groups. Further, although rating cannot create new models, rating seems quicker and easier than revision and yielded significantly better models – so, we suggest efficient BN amalgamation should include both

Virtuous opinion change in structured groups

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed.</p

Improving the production and evaluation of structural models using a Delphi process

Bayes Nets (BNs) are extremely useful for causal and probabilistic modelling in many real-world applications, often built with information elicited from groups of domain experts. But their potential for reasoning and decision support has been limited by two major factors: the need for significant normative knowledge, and the lack of any validated methods or software supporting collaboration. Consequently, we have developed a web-based structured technique – Bayesian Argumentation via Delphi (BARD) – to enable groups of domain experts to receive minimal normative training and then collaborate effectively to produce high-quality BNs. BARD harnesses multiple perspectives on a problem, while minimising biases manifest in freely interacting groups, via a Delphi process: solutions are first produced individually, then shared, followed by an opportunity for individuals to revise their solutions. To test the hypothesis that BNs improve due to Delphi, we conducted an experiment whereby individuals with a little BN training and practice produced structural models using BARD for two Bayesian reasoning problems. Participants then received 6 other structural models for each problem, rated their quality on a 7-point scale, and revised their own models if they wished. Both top-rated and revised models were on average significantly better quality (scored against a gold-standard) than the initial models, with large and medium effect sizes. We conclude that Delphi – and BARD – improves the quality of BNs produced by groups. Further, although rating cannot create new models, rating seems quicker and easier than revision and yielded significantly better models – so, we suggest efficient BN amalgamation should include both