scholarly journals Human biases limit algorithmic boosts of cultural evolution.

2021 ◽  
Author(s):  
Levin Brinkmann ◽  
Deniz Gezerli ◽  
KIRA VON KLEIST ◽  
Thomas Franz Müller ◽  
Iyad Rahwan ◽  
...  
Keyword(s):  
Problem Solving ◽  
Cultural Evolution ◽  
Task Environment ◽  
Improve Performance ◽  
Agent Based ◽  
Transmission Of Knowledge ◽  
Transmission Chain ◽  
Human Participant ◽  
Problem Solving Strategies ◽  
The Many

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases that enable solutions and behaviours to spread socially from one human to the next, selecting from whom we copy and what we copy. In a digital society, algorithmic and human agents both contribute to transmission of knowledge. One hypothesis is that machines may influence the patterns of social transmission not only by providing a means for spreading human behavior but also by providing novel behaviors themselves. We propose that certain algorithms might show (either by learning or by design) different behaviors, biases and problem-solving abilities than their human counterparts. This may in turn foster better decisions in environments where diversity in problem-solving strategies is beneficial. In this study, we ask whether machines with complementary biases to humans could boost cultural evolution in a lab-based planning task, where humans show suboptimal biases. We conducted a large behavioral study and an agent-based simulation to test the performance of transmission chains with human and machine players. In half of the chains, an algorithmic bot replaced a human participant. We show that the bot boosts the performance of immediately following participants in the chain, but this gain is lost for participants further down the transmission chain. Our findings suggest that machines can potentially improve performance, but human bias can hinder machine solutions from being preserved, especially under conditions of uncertainty or high cognitive load. Our results suggest that the conditions for hybrid social learning and cultural evolution may be limited by task environment and human biases.

scholarly journals Designing Like a Human: Simulating Cognitive Style in Teamwork with an Agent-Based Model

2019 ◽  
Author(s):  
Samuel Lapp
Keyword(s):  
Problem Solving ◽  
Cognitive Style ◽  
Cognitive Styles ◽  
Performance Testing ◽  
Collaborative Problem Solving ◽  
Agent Based Model ◽  
Improve Performance ◽  
Agent Based ◽  
Race Car ◽  
Collaborative Problem

This thesis describes the development of an agent-based model for simulating cognitive style in the context of collaborative problem solving. Cognitive style describes the diverse ways in which people solve problems. Individuals’ cognitive styles can impact the success or failure of a design team. However, the effects of cognitive style in collaborative problem solving are not well understood. To address this gap, this thesis presents KABOOM (KAI Agent-Based Organizational Optimization Model), the first agent-based model of teamwork to incorporate cognitive style. In this thesis, experiments using KABOOM investigate the interacting effects of a design team’s communication patterns, specialization, and cognitive style composition on a team’s performance. Testing the model with a race car design problem reveals that teams can strategically leverage diversity of cognitive style to improve performance. By simulating cognitive style and team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.

IDENTIFICATION OF PROBLEM SOLVING STRATEGIES IN MATHEATICS AMONG HIGH SCHOOL STUDENTS

2017 ◽  
Vol 4 (36) ◽  
Author(s):  
J. Navaneetha Krishnan ◽  
P. Paul Devanesan
Keyword(s):  
High School ◽  
High School Students ◽  
Problem Solving ◽  
Sample Survey ◽  
Survey Method ◽  
Teaching Mathematics ◽  
School Students ◽  
Problem Solving Strategies ◽  
Achievement In Mathematics

The major aim of teaching Mathematics is to develop problem solving skill among the students. This article aims to find out the problem solving strategies and to test the students’ ability in using these strategies to solve problems. Using sample survey method, four hundred students were taken for this investigation. Students’ achievement in solving problems was tested for their Identification and Application of Problem Solving Strategies as a major finding, thirty one percent of the students’ achievement in mathematics is contributed by Identification and Application of Problem Solving Strategies.

scholarly journals Using process data to understand problem-solving strategies and processes for drag-and-drop items in a large-scale mathematics assessment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yang Jiang ◽  
Tao Gong ◽  
Luis E. Saldivia ◽  
Gabrielle Cayton-Hodges ◽  
Christopher Agard
Keyword(s):  
Problem Solving ◽  
Time Allocation ◽  
Large Scale ◽  
Solution Process ◽  
Process Data ◽  
Mathematics Assessments ◽  
Assessment Design ◽  
Eighth Grade Students ◽  
Problem Solving Strategies ◽  
Drag And Drop

AbstractIn 2017, the mathematics assessments that are part of the National Assessment of Educational Progress (NAEP) program underwent a transformation shifting the administration from paper-and-pencil formats to digitally-based assessments (DBA). This shift introduced new interactive item types that bring rich process data and tremendous opportunities to study the cognitive and behavioral processes that underlie test-takers’ performances in ways that are not otherwise possible with the response data alone. In this exploratory study, we investigated the problem-solving processes and strategies applied by the nation’s fourth and eighth graders by analyzing the process data collected during their interactions with two technology-enhanced drag-and-drop items (one item for each grade) included in the first digital operational administration of the NAEP’s mathematics assessments. Results from this research revealed how test-takers who achieved different levels of accuracy on the items engaged in various cognitive and metacognitive processes (e.g., in terms of their time allocation, answer change behaviors, and problem-solving strategies), providing insights into the common mathematical misconceptions that fourth- and eighth-grade students held and the steps where they may have struggled during their solution process. Implications of the findings for educational assessment design and limitations of this research are also discussed.

scholarly journals Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 681
Author(s):  
László Barna Iantovics
Keyword(s):  
Problem Solving ◽  
Scientific Literature ◽  
Real Life ◽  
Intelligent Agent ◽  
Difficult Problem ◽  
Black Box ◽  
Machine Intelligence ◽  
Agent Based ◽  
Np Hard Problem ◽  
Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

scholarly journals Teachers’ Use of Technology Affordances to Contextualize and Dynamically Enrich and Extend Mathematical Problem-Solving Strategies

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 793
Author(s):  
Manuel Santos-Trigo ◽  
Fernando Barrera-Mora ◽  
Matías Camacho-Machín
Keyword(s):  
Problem Solving ◽  
High School Teachers ◽  
Digital Technology ◽  
Dynamic Models ◽  
Mathematical Problem ◽  
Mathematical Problem Solving ◽  
School Teachers ◽  
Use Of Technology ◽  
Technology Affordances ◽  
Problem Solving Strategies

This study aims to document the extent to which the use of digital technology enhances and extends high school teachers’ problem-solving strategies when framing their teaching scenarios. The participants systematically relied on online developments such as Wikipedia to contextualize problem statements or to review involved concepts. Likewise, they activated GeoGebra’s affordances to construct and explore dynamic models of tasks. The Apollonius problem is used to illustrate and discuss how the participants contextualized the task and relied on technology affordances to construct and explore problems’ dynamic models. As a result, they exhibited and extended the domain of several problem-solving strategies including the use of simpler cases, dragging orderly objects, measuring objects attributes, and finding loci of some objects that shaped their approached to reasoning and solve problems.

scholarly journals Thinking Process of Naive Problem Solvers to Solve Mathematical Problems

2016 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Jackson Pasini Mairing
Keyword(s):  
Problem Solving ◽  
Procedural Knowledge ◽  
Mental Image ◽  
Research Subjects ◽  
Maximum Score ◽  
Mathematical Problems ◽  
Problem Solving Strategies ◽  
Thinking Process ◽  
Problem Solvers ◽  
Holistic Rubric

Solving problem is not only a goal of mathematical learning. Students acquire ways of thinking, habits of persistence and curiosity, and confidence in unfamiliar situations by learning to solve problems. In fact, there were students who had difficulty in solving problems. The students were naive problem solvers. This research aimed to describe the thinking process of naive problem solvers based on heuristic of Polya. The researcher gave two problems to students at grade XI from one of high schools in Palangka Raya, Indonesia. The research subjects were two students with problem solving scores of 0 or 1 for both problems (naive problem solvers). The score was determined by using a holistic rubric with maximum score of 4. Each subject was interviewed by the researcher separately based on the subject’s solution. The results showed that the naive problem solvers read the problems for several times in order to understand them. The naive problem solvers could determine the known and the unknown if they were written in the problems. However, they faced difficulties when the information in the problems should be processed in their mindsto construct a mental image. The naive problem solvers were also failed to make an appropriate plan because they did not have a problem solving schema. The schema was constructed by the understanding of the problems, conceptual and procedural knowledge of the relevant concepts, knowledge of problem solving strategies, and previous experiences in solving isomorphic problems.

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