The role of decision confidence in advice-taking and trust formation

In a world where ideas flow freely between people across multiple platforms, we often find ourselves relying on others' information without an objective standard to judge whether those opinions are accurate. The present study tests an agreement-in-confidence hypothesis of advice perception, which holds that internal metacognitive evaluations of decision confidence play an important functional role in the perception and use of social information, such as peers' advice. We propose that confidence can be used, computationally, to estimate advisors' trustworthiness and advice reliability. Specifically, these processes are hypothesized to be particularly important in situations where objective feedback is absent or difficult to acquire. Here, we use a judge-advisor system paradigm to precisely manipulate the profiles of virtual advisors whose opinions are provided to participants performing a perceptual decision making task. We find that when advisors' and participants' judgments are independent, people are able to discriminate subtle advice features, like confidence calibration, whether or not objective feedback is available. However, when observers' judgments (and judgment errors) are correlated - as is the case in many social contexts - predictable distortions can be observed between feedback and feedback-free scenarios. A simple model of advice reliability estimation, endowed with metacognitive insight, is able to explain key patterns of results observed in the human data. We use agent-based modeling to explore implications of these individual-level decision strategies for network-level patterns of trust and belief formation

A Brief Taxonomy of Hybrid Intelligence

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field

On the use of metacognitive signals to navigate the social world

Since the early days of psychology, practitioners have recognised that metacognition - or the act of thinking about oneâs own thinking - is intertwined with our experience of the world. In the last decade, scientists have started to understand metacognitive signals, like judgments of confidence, as precise mathematical constructs. Confidence can be conceived of as an internal estimate of the probability of being correct. As such, confidence influences both advice seeking and advice taking while allowing people to optimally combine their views for joint action and group coordination. This work begins by exploring the idea that confidence judgments are important for monitoring not only uncertainty associated with oneâs performance but also, thanks to their positive covariation with accuracy, the reliability of social advisers, particularly when objective criteria are not available. I present data showing that, when adviser and adviseeâs judgments are independent, people are able to detect subtle variations in advice information, irrespective of feedback presence. I also show that, when such independence is broken, the use of subjective confidence to track othersâ reliability leads to systematic deviations. I then proceed to explore the differences existing between static and dynamic social information exchange. Traditionally, social and organisational psychology have investigated one-step unidirectional information systems, but many real-life interactions happen on a continuous time-scale, where social exchanges are recursive and dynamic. I present results indicating that the dynamics of social information exchange (recursive vs. one-step) affect individual opinions over and above the information that is communicated. Overall, my results suggest a bidirectional involvement of confidence in social inference and information exchange, and highlight the limits of the mechanisms underlying it.</p

On the use of metacognitive signals to navigate the social world

Since the early days of psychology, practitioners have recognised that metacognition - or the act of thinking about one’s own thinking - is intertwined with our experience of the world. In the last decade, scientists have started to understand metacognitive signals, like judgments of confidence, as precise mathematical constructs. Confidence can be conceived of as an internal estimate of the probability of being correct. As such, confidence influences both advice seeking and advice taking while allowing people to optimally combine their views for joint action and group coordination. This work begins by exploring the idea that confidence judgments are important for monitoring not only uncertainty associated with one’s performance but also, thanks to their positive covariation with accuracy, the reliability of social advisers, particularly when objective criteria are not available. I present data showing that, when adviser and advisee’s judgments are independent, people are able to detect subtle variations in advice information, irrespective of feedback presence. I also show that, when such independence is broken, the use of subjective confidence to track others’ reliability leads to systematic deviations. I then proceed to explore the differences existing between static and dynamic social information exchange. Traditionally, social and organisational psychology have investigated one-step unidirectional information systems, but many real-life interactions happen on a continuous time-scale, where social exchanges are recursive and dynamic. I present results indicating that the dynamics of social information exchange (recursive vs. one-step) affect individual opinions over and above the information that is communicated. Overall, my results suggest a bidirectional involvement of confidence in social inference and information exchange, and highlight the limits of the mechanisms underlying it.</p

Benefits of spontaneous confidence alignment between dyad members

In many domains, imitating others’ behaviour can help individuals to solve problems that would be too difficult or too complex for the individuals. In collective decision making tasks, people have been shown to use confidence as a means to communicate the uncertainty surrounding internal noisy estimates. Here, we show that confidence alignment, namely, shifting average confidence between dyad members towards each other, naturally emerges when interacting with others’ opinions. This alignment has a measurable impact on group performance as well as the accuracy of individual members following information exchange. It is suggested that confidence alignment arises among individuals from the necessity of minimising confidence variation arising from task-unrelated variables (trait confidence), while at the same time maximising variation arising from stimulus characteristics (state confidence)

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives