scholarly journals Human Biases Limit Cumulative Innovation

2021 ◽  
Author(s):  
Bill Thompson ◽  
Tom Griffiths
Keyword(s):  
Cultural Evolution ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Human Learning ◽  
Human Cognition ◽  
Technological Advancement ◽  
Laboratory Setting ◽  
Transmission Chain ◽  
Large N ◽  
Cumulative Innovation

Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large (n = 1,250) behavioral study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific, and educational contexts.

scholarly journals Human biases limit cumulative innovation

2021 ◽  
Vol 288 (1946) ◽  
pp. 20202752
Author(s):  
Bill Thompson ◽  
Thomas L. Griffiths
Keyword(s):  
Mathematical Model ◽  
Cultural Evolution ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Human Learning ◽  
Human Cognition ◽  
Technological Advancement ◽  
Laboratory Setting ◽  
Transmission Chain ◽  
Cumulative Innovation

Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large ( n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

Cultural evolution and cultural psychology

2018 ◽  
Author(s):  
Alex Mesoudi
Keyword(s):  
Cultural History ◽  
Cultural Evolution ◽  
Cultural Transmission ◽  
Cultural Psychology ◽  
Phylogenetic Analyses ◽  
Cultural Dimensions ◽  
Human Cognition ◽  
Cultural Variation ◽  
Cultural Inheritance ◽  
Human Psychology

Cultural evolution is a branch of the evolutionary sciences which assumes that (i) human cognition and behaviour is shaped not only by genetic inheritance, but also cultural inheritance (also known as social learning), and (ii) this cultural inheritance constitutes a Darwinian evolutionary system that can be analysed and studied using tools borrowed from evolutionary biology. In this chapter I explore the numerous compatibilities between the fields of cultural evolution and cultural psychology, and the potential mutual benefits from their closer alignment. First, understanding the evolutionary context within which human psychology emerged gives added significance to the findings of cultural psychologists, which reinforce the conclusion reached by cultural evolution scholars that humans inhabit a ‘cultural niche’ within which the major means of adaptation to difference environments is cultural, rather than genetic. Hence, we should not be surprised that human psychology shows substantial cross-cultural variation. Second, a focus on cultural transmission pathways, drawing on cultural evolution models and empirical research, can help to explain to the maintenance of, and potential changes in, cultural variation in psychological processes. Evidence from migrants, in particular, points to a mix of vertical, oblique and horizontal cultural transmission that can explain the differential stability of different cultural dimensions. Third, cultural evolutionary methods offer powerful means of testing historical (“macro-evolutionary”) hypotheses put forward by cultural psychologists for the origin of psychological differences. Explanations in terms of means of subsistence, rates of environmental change or pathogen prevalence can be tested using quantitative models and phylogenetic analyses that can be used to reconstruct cultural lineages. Evolutionary considerations also point to potential problems with current cross-country comparisons conducted within cultural psychology, such as the non-independence of data points due to shared cultural history. Finally, I argue that cultural psychology can play a central role in a synthetic evolutionary science of culture, providing valuable links between individual-oriented disciplines such as experimental psychology and neuroscience on the one hand, and society-oriented disciplines such as anthropology, history and sociology on the other, all within an evolutionary framework that provides links to the biological sciences.

scholarly journals From storytelling to Facebook. Content biases when retelling or sharing a story

2021 ◽  
Author(s):  
Alberto Acerbi
Keyword(s):  
Social Network ◽  
Cultural Evolution ◽  
Cultural Transmission ◽  
Specific Content ◽  
Oral Storytelling ◽  
Oral Transmission ◽  
Related Information ◽  
Transmission Chain ◽  
Set Up ◽  
Content Information

Cultural evolution researchers use transmission chain experiments to investigate which content is more likely to survive when transmitted from one individual to another. These experiments resemble oral storytelling, where individuals need to understand, memorise, and reproduce the content. However, prominent contemporary forms of cultural transmission—think an online sharing— only involve the willingness to transmit the content. Here I present two fully preregistered online experiments that explicitly investigated the differences between these two modalities of transmission. The first experiment (N=1080) examined whether negative content, information eliciting disgust, and threat-related information were better transmitted than their neutral counterpart in a traditional transmission chain set-up. The second experiment (N=1200), used the same material, but participants were asked whether they would share or not the content in two conditions: in a large anonymous social network, or with their friends, in their favourite social network. Negative content was both better transmitted in transmission chain experiments and shared more than its neutral counterpart. Threat-related information was successful in transmission chain experiments but not when sharing, and, finally, information eliciting disgust was not advantaged in either. Overall, the results present a composite picture, suggesting that the interactions between the specific content and the medium of transmission are important and, possibly, that content biases are stronger when memorisation and reproduction are involved in the transmission—like in oral transmission—than when they are not—like in online sharing.

Towards a Re-Definition of Government Interpreters' Agency Against a Backdrop of Sociopolitical and Cultural Evolution

2018 ◽  
pp. 238-257 ◽  
Author(s):  
Chonglong Gu
Keyword(s):  
Cultural Evolution ◽  
Actual Practice ◽  
Press Conference ◽  
Technological Advancement ◽  
National Interests ◽  
Global Engagement ◽  
Definition Of ◽  
Reform And Opening Up ◽  
Opening Up ◽  
The Way

The sociopolitical and cultural evolution as a result of the Reform and Opening up in 1978, facilitated not least by the inexorable juggernaut of globalization and technological advancement, has revolutionized the way China engages domestically and interacts with the outside world. The need for more proactive diplomacy and open engagement witnessed the institutionalization of the interpreter-mediated premier's press conferences. Such a discursive event provides a vital platform for China to articulate its discourse and rebrand its image in tandem with the profound changes signaled by the Dengist reform. This chapter investigates critically how political press conference interpreting and interpreters' agency in China are impacted in relation to such dramatic transformations. It is revealed that, while interpreters are confronted with seemingly conflicting expectations, in actual practice they are often able to negotiate a way as highly competent interpreting professionals with the additional missions of advancing China's global engagement and safeguarding China's national interests.

scholarly journals A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5679
Author(s):  
Mohamed A. M. Shaheen ◽  
Dalia Yousri ◽  
Ahmed Fathy ◽  
Hany M. Hasanien ◽  
Abdulaziz Alkuhayli ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Electric Power ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Reactive Power ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Marine Predators

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

scholarly journals Integer Programming Formulations for Approximate Packing Circles in a Rectangular Container

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Igor Litvinchev ◽  
Edith Lucero Ozuna Espinosa
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Packing Problem ◽  
Regular Grid ◽  
Food Industries ◽  
Rectangular Container ◽  
Binary Problem ◽  
Weighted Number ◽  
Grid Nesting

A problem of packing a limited number of unequal circles in a fixed size rectangular container is considered. The aim is to maximize the (weighted) number of circles placed into the container or minimize the waste. This problem has numerous applications in logistics, including production and packing for the textile, apparel, naval, automobile, aerospace, and food industries. Frequently the problem is formulated as a nonconvex continuous optimization problem which is solved by heuristic techniques combined with local search procedures. New formulations are proposed for approximate solution of packing problem. The container is approximated by a regular grid and the nodes of the grid are considered as potential positions for assigning centers of the circles. The packing problem is then stated as a large scale linear 0-1 optimization problem. The binary variables represent the assignment of centers to the nodes of the grid. Nesting circles inside one another is also considered. The resulting binary problem is then solved by commercial software. Numerical results are presented to demonstrate the efficiency of the proposed approach and compared with known results.

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.

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