A5060034437- Opinion Dynamics and Social Influence
- Explainable Artificial Intelligence (XAI)
- Computational and Text Analysis Methods
- Advanced Multi-Objective Optimization Algorithms
- Language and cultural evolution
- Optimal Experimental Design Methods
- Scientific Computing and Data Management
- Mental Health Research Topics
- Statistical Methods in Clinical Trials
- Social Media and Politics
- Evolutionary Game Theory and Cooperation
- Social and Intergroup Psychology
- Neural Networks and Applications
- Probabilistic and Robust Engineering Design
- Machine Learning and Data Classification
- Advanced Bandit Algorithms Research
- Meta-analysis and systematic reviews
- Survey Sampling and Estimation Techniques
- Decision-Making and Behavioral Economics
- Machine Learning in Materials Science
- Electoral Systems and Political Participation
- Fuzzy Logic and Control Systems
- Cultural Differences and Values
- Health and Medical Research Impacts
- Complex Systems and Decision Making
University of Manchester
2023-2025
TU Dortmund University
2024
Decision Sciences (United States)
2019-2023
Carnegie Mellon University
2019-2023
California State University, Fullerton
2023
John Brown University
2019
Brown University
2019
Hologic (Germany)
2019
Chapman University
2019
McGill University
2016
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, overcoming traditional impediments to progress. This article evaluates scope automation within practice assesses recent approaches. Furthermore, it discusses different perspectives following questions: where do greatest...
The preference for simpler explanations, known as the parsimony principle, has long guided development of scientific theories, hypotheses, and models. Yet recent years have seen a number successes in employing highly complex models inquiry (e.g., 3D protein folding or climate forecasting). In this paper, we re-examine principle light these technological advancements. We review developments, including surprising benefits modeling with more parameters than data, increasing appreciation...
Abstract In many areas of the social and behavioral sciences, nature experiments theories that best capture underlying constructs are themselves active inquiry. Integrative experiment design risks being prematurely exploitative, hindering exploration experimental paradigms diverse theoretical accounts for target phenomena.
Abstract Adaptive design optimization (ADO) is a state-of-the-art technique for experimental (Cavagnaro et al., 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about hypothetical construct of interest (e.g., parameters cognitive model). To calculate this leverages modeler’s existing knowledge, specified form prior distribution. Informative priors align with distribution focal participant population. This alignment assumed by ADO’s internal...
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, overcoming traditional impediments to progress. This article evaluates scope automation within practice assesses recent approaches. Furthermore, it discusses different perspectives following questions: Where do greatest...
Subjective measures of overall quality life are built in to numerous surveys Canada and around the world, increasingly analyzed used as indicators human well-being social progress. Yet, even Canada, federal exclude Aboriginal peoples on-reserve and, general, there very few data sources on satisfaction among respondents. We report two exceptional that do solicit assessments from respondents, compare inferences these general Canadian population. generally find comparable effects objective...
How do people use information from others to solve complex problems? Prior work has addressed this question by placing in social learning situations where the problems they were asked required varying degrees of exploration. This past uncovered important interactions between groups' connectivity and problem's complexity: advantage less connected networks over more increased as exploration was increasingly for optimally solving problem at hand. We propose Social Interpolation Model (SIM), an...
Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters. Prior work has shown that other forms learning can suffer from bias, where unrepresentative sampling leads inconsistent parameter estimates. We show bias also afflict design, depending on model misspecification. analyze case estimating linear model, and worse misspecification implies more severe bias. At same time, classes incorporating...
Previous work has demonstrated that certain speech patterns vary systematically between sociodemographic groups, so in some cases the way a person speaks is valid cue to group membership. Our addresses whether or not participants use these linguistic cues when assessing speaker’s likely political identity. We database of speeches by U.S. Congressional representatives isolate words are statistically diagnostic party In series four studies, we demonstrate participants’ judgments track...
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, environments, and problem structures. Going beyond searching “intelligent” collectives, we integrate research from different disciplines outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail reach seemingly obvious solutions, how they change their strategies network...
Humans can learn individual episodes and generalizable rules also successfully retain both kinds of acquired knowledge over time. In the cognitive science literature, (1) learning (2) remembering are often conceptualized as competing processes that necessitate separate, complementary systems. Inspired by recent research in statistical learning, we challenge these trade-offs, hypothesizing they arise from capacity limitations rather than inherent incompatibility underlying processes. Using an...
Generalization outside the scope of one's training data requires leveraging prior knowledge about effects that transfer, and don't, between different sources. Bayesian transfer learning is a principled paradigm for specifying this knowledge, refining it on basis from source (training) target (prediction) tasks. We address challenging setting where learner (i) cannot fine-tune in task, (ii) does not know which points correspond to same task (i.e., sources are unknown). propose proxy-informed...
Previous work has demonstrated that certain speech patterns vary systematically between sociodemographic groups, so in some cases the way a person speaks is valid cue to group membership. Our addresses whether or not participants use these linguistic cues when assessing speaker’s likely political identity. We database of speeches by U.S. Congressional representatives isolate words are statistically diagnostic party In series four studies, we demonstrate participants’ judgments align with...
Approaching issues through the lens of non-negotiable values increases perceived intractability debate (Baron & Spranca, 1997), while focusing on concrete consequences policies instead results in moderation extreme opinions (Fernbach et al., 2013) and greater likelihood conflict resolution Leshner, 2000). Using comments popular social media platform Reddit from January 2006 until September 2017, we show how changes framing same-sex marriage public discourse relate to opinion. We use...
We present an empirical demonstration that people rely on linguistic valence as a direct cue to speaker’s group membership. Members of the U.S. voting public judge positive words more likely be spoken by members their political in-group, and negative out-group (three studies with 655 participants). further find participants perceive pluralized forms nouns extremely valenced than singular (one study 280 This allowed us control for semantic content while eliciting systematic differences in...
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions. However, relevance of variables is not necessarily known beforehand. Moreover, can sometimes be optimized themselves at additional cost, a setting overlooked by current CBO algorithms. Cost-sensitive would simply include optimizable part based on their cost....
Adaptive design optimization (ADO) is a state-of-the-art technique for experimental (Cavagnaro, Myung, Pitt, & Kujala, 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about hypothetical construct of interest (e.g., parameters cognitive model). To calculate this leverages modeler's existing knowledge, specified form prior distribution. Informative priors align with distribution focal participant population. This alignment assumed by ADO's internal...
In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated estimation of target parameter, no intrinsic interest in characterizing entire data-generating process. Usually, must also contend with additional sources uncertainty or variables -- nuisance parameters. Bayesian active sequential optimal experimental design, straightforwardly accommodate presence parameters, so is natural learning framework for...