We study the wisdom of the crowd in three sequential decision‐making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior‐based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. We also (...) consider model‐based approaches to each task. This involves inferring cognitive models for each individual based on their observed behavior, and using these models to predict what each individual would do in any possible task situation. We show that this approach performs robustly well for all three tasks and has the additional advantage of being able to generalize to new problems for which there are no behavioral data. We discuss potential applications of the model‐based approach to real‐world sequential decision problems and discuss how our approach contributes to the understanding of collective intelligence. (shrink)

Humans routinely transmit and interpret subtle information about their mental states through the language they use, even when only the language text is available. This suggests humans can utilize the linguistic signature of a mental state, comprised of features in the text. Once the relevant features are identified, mindprints can be used to automatically identify mental states communicated via language. We focus on the mindprints of eight mental states resulting from intentions, attitudes, and emotions, and present a mindprint-based machine learning (...) technique to automatically identify these mental states in realistic language data. By using linguistic features that leverage available semantic, syntactic, and valence information, our approach achieves near-human performance on average and even exceeds human performance on occasion. Given this, we believe mindprints could be very valuable for intelligent systems interacting linguistically with humans. Keywords: mental state; linguistic features; mindprint; natural language processing; information extraction. (shrink)

Purpose Children are becoming heavy users of communication and information technologies from an early age. These technologies carry risks to which children may be exposed. In collaboration with the Israel Ministry of Education, the authors launched a week-long safe online awareness program for school children in 257 elementary and middle schools in Israel. Each class independently composed a safe and ethical code of online behavior following two classroom debate sessions. The purpose of this study was to analyze these codes and (...) learn how school children perceive and understand the proper use of the network using thematic analysis. Design/methodology/approach A total of 8,181 students between the ages of 8 and 14 years in 303 classes from 257 schools participated in the program. These classes composed 303 ethical codes, which were decomposed into 2,201 elements. Using mixed-methods research combining quantitative and qualitative methodologies, the elements of the ethical codes were analyzed, interpreted, and classified to identify the dominant themes and discourses used by the students when addressing issues related to safe online use. Findings Findings indicate that Israeli students are aware of the dangers and risks of the internet, and these concerns are reflected in their own ethical codes. The students discouraged online self-exposure and encouraged precautions and wariness towards members of out-groups. The themes included sentences which asked for responsible, appropriate, and lawful use, expressed concern about privacy issues, and stated the need for adult involvement. Most of them reflected an “us against them ” perspective. Originality/value The current study presents an innovative “bottom-up” program based on wisdom of the crowd approach, that can be implemented in schools internationally in order to encourage reflexivity and teach children the necessary skills for safe online experiences. In addition, this study analyses the school children’s own views of the dangers of social media and learn about their perspective and understanding of internet use. (shrink)

We apply the “wisdom of the crowd” idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model‐based method that predicts (...) the categorization behavior people would produce for new stimuli, based on their behavior with observed stimuli, and uses the majority of these predicted decisions. We demonstrate and evaluate the model‐based approach in two case studies. In the first, we use the general recognition theory decision‐bound model of categorization (Ashby & Townsend, ) to infer each person's decision boundary for two categories of perceptual stimuli, and we use these inferences to make aggregated predictions about new stimuli. In the second, we use the generalized context model exemplar model of categorization (Nosofsky, ) to infer each person's selective attention for face stimuli, and we use these inferences to make aggregated predictions about withheld stimuli. In both case studies, we show that our method successfully predicts the category of unobserved stimuli, and we emphasize that the aggregated crowd decisions arise from psychologically interpretable processes and parameters. We conclude by discussing extensions and potential real‐world applications of the approach. (shrink)

ABSTRACTWith over 560 citations reported on Google Scholar by April 2018, a publication by Juslin and Gabrielsson presented evidence supporting performers’ abilities to communicate, with hig...

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)