東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A computational model of memetic evo...

Welsh, Noah H.

Linked to FindBook

Google Book

Amazon

博客來

A computational model of memetic evolution: Optimizing collective intelligence.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A computational model of memetic evolution: Optimizing collective intelligence./
Author:	Welsh, Noah H.
Description:	109 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: A.
Contained By:	Dissertation Abstracts International75-10A(E).
Subject:	Education, Leadership. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3624009
ISBN:	9781303969355

A computational model of memetic evolution: Optimizing collective intelligence.
Welsh, Noah H.

A computational model of memetic evolution: Optimizing collective intelligence. - 109 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: A.

Thesis (Ph.D.)--Clemson University, 2014.

The purpose of this study was to create an adaptive agent based simulation modeling the processes of creative collaboration. This model aided in the development of a new evolutionary based framework through which education scholars, academics, and professionals in all disciplines and industries can work to optimize their collective ability to find creative solutions to complex problems. The basic premise follows that the process of idea exchange, parallels the role sexual reproduction in biological evolution and is essential to society's collective ability to solve complex problems. The study outlined a set of assumptions used to develop a new theory of collective intelligence. These assumptions were then translated into design requirements that were designated as parameters for a computational simulation that utilizes two types of machine learning algorithms. This model was developed, and 200 simulations were run for each of 48 different combinations of four independent variables for a total of 9,600 simulations. Statistical analysis of the data revealed a number of patterns enhancing the simulation agents' collective problem solving abilities. Most notably, agents' collective problem solving abilities were optimized when idea exchange between agents was balanced with individual agent time contemplating new creative strategies. Additionally, the agents' collective problem solving abilities were optimized when simulation constraints did not force the agents to converge upon one potential solution.

ISBN: 9781303969355Subjects--Topical Terms:

1035576
Education, Leadership.

A computational model of memetic evolution: Optimizing collective intelligence.
LDR:02427nmm a2200289 4500 001 2055361
005 20141203121524.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781303969355
035 $a (MiAaPQ)AAI3624009
035 $a AAI3624009
040 $a MiAaPQ $c MiAaPQ
100 1 $a Welsh, Noah H. $3 3169011
245 1 2 $a A computational model of memetic evolution: Optimizing collective intelligence.
300 $a 109 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: A.
500 $a Advisers: Russ Marion; Joshua Summers.
502 $a Thesis (Ph.D.)--Clemson University, 2014.
520 $a The purpose of this study was to create an adaptive agent based simulation modeling the processes of creative collaboration. This model aided in the development of a new evolutionary based framework through which education scholars, academics, and professionals in all disciplines and industries can work to optimize their collective ability to find creative solutions to complex problems. The basic premise follows that the process of idea exchange, parallels the role sexual reproduction in biological evolution and is essential to society's collective ability to solve complex problems. The study outlined a set of assumptions used to develop a new theory of collective intelligence. These assumptions were then translated into design requirements that were designated as parameters for a computational simulation that utilizes two types of machine learning algorithms. This model was developed, and 200 simulations were run for each of 48 different combinations of four independent variables for a total of 9,600 simulations. Statistical analysis of the data revealed a number of patterns enhancing the simulation agents' collective problem solving abilities. Most notably, agents' collective problem solving abilities were optimized when idea exchange between agents was balanced with individual agent time contemplating new creative strategies. Additionally, the agents' collective problem solving abilities were optimized when simulation constraints did not force the agents to converge upon one potential solution.
590 $a School code: 0050.
650 4 $a Education, Leadership. $3 1035576
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Computer Science. $3 626642
690 $a 0449
690 $a 0790
690 $a 0984
710 2 $a Clemson University. $b Education. $3 1022688
773 0 $t Dissertation Abstracts International $g 75-10A(E).
790 $a 0050
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3624009