Artificial intelligence and economic planning
The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence (AI). We conclude that central planning couple...
Gespeichert in:
Veröffentlicht in: | AI & society 2024-06, Vol.39 (3), p.985-1007 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1007 |
---|---|
container_issue | 3 |
container_start_page | 985 |
container_title | AI & society |
container_volume | 39 |
creator | Gmeiner, Robert Harper, Mario |
description | The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence (AI). We conclude that central planning coupled with AI is still unable to allocate resources with the same efficiency as price signals and market forces through examinations of the technical structure of current AI approaches. AI-driven central planning is not viable in part due to incentives, computing power, knowledge/data acquisition, and speed of collection. There are deep incentive problems for planners, programmers, and ordinary participants to complicate efforts at planning and bias data. Most importantly, AI cannot easily or quickly duplicate the signals of relative scarcity that are generated in markets. Some challenges we highlight are pertinent to planning generally, but many others arise from the introduction of an AI-planner. |
doi_str_mv | 10.1007/s00146-022-01523-x |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3073390492</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3073390492</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-4d41e23baf0936966fc647049b5a71af64ecd2cba5538f192c524d3e1f04482d3</originalsourceid><addsrcrecordid>eNp9kE1LAzEURYMoWKt_wNWA6-jL50yWpagVCm50HTKZZEiZZmoyhfrvjY7gztW7i3vug4PQLYF7AlA_ZADCJQZKMRBBGT6doQXhTGAhhThHC1CClCzlJbrKeQcAUjR0gfAqTcEHG8xQhTi5YQi9i9ZVJnaVs2Mc98FWh8HEGGJ_jS68GbK7-b1L9P70-Lbe4O3r88t6tcWWETVh3nHiKGuNB8WkktJbyWvgqhWmJsZL7mxHbWuEYI0nilpBeccc8cB5Qzu2RHfz7iGNH0eXJ70bjymWl5pBzZgqW7S06Nyyacw5Oa8PKexN-tQE9LcWPWvRRYv-0aJPBWIzlEs59i79Tf9DfQHhqmSC</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3073390492</pqid></control><display><type>article</type><title>Artificial intelligence and economic planning</title><source>SpringerLink Journals - AutoHoldings</source><creator>Gmeiner, Robert ; Harper, Mario</creator><creatorcontrib>Gmeiner, Robert ; Harper, Mario</creatorcontrib><description>The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence (AI). We conclude that central planning coupled with AI is still unable to allocate resources with the same efficiency as price signals and market forces through examinations of the technical structure of current AI approaches. AI-driven central planning is not viable in part due to incentives, computing power, knowledge/data acquisition, and speed of collection. There are deep incentive problems for planners, programmers, and ordinary participants to complicate efforts at planning and bias data. Most importantly, AI cannot easily or quickly duplicate the signals of relative scarcity that are generated in markets. Some challenges we highlight are pertinent to planning generally, but many others arise from the introduction of an AI-planner.</description><identifier>ISSN: 0951-5666</identifier><identifier>EISSN: 1435-5655</identifier><identifier>DOI: 10.1007/s00146-022-01523-x</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Algorithms ; Artificial Intelligence ; Computation ; Computer Science ; Computers ; Control ; Data acquisition ; Economic planning ; Engineering Economics ; Logistics ; Machine learning ; Marketing ; Mechatronics ; Methodology of the Social Sciences ; Open Forum ; Organization ; Performing Arts ; Robotics ; Society ; Stagnation ; Tacit knowledge</subject><ispartof>AI & society, 2024-06, Vol.39 (3), p.985-1007</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-4d41e23baf0936966fc647049b5a71af64ecd2cba5538f192c524d3e1f04482d3</citedby><cites>FETCH-LOGICAL-c319t-4d41e23baf0936966fc647049b5a71af64ecd2cba5538f192c524d3e1f04482d3</cites><orcidid>0000-0002-7470-6167</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00146-022-01523-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00146-022-01523-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Gmeiner, Robert</creatorcontrib><creatorcontrib>Harper, Mario</creatorcontrib><title>Artificial intelligence and economic planning</title><title>AI & society</title><addtitle>AI & Soc</addtitle><description>The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence (AI). We conclude that central planning coupled with AI is still unable to allocate resources with the same efficiency as price signals and market forces through examinations of the technical structure of current AI approaches. AI-driven central planning is not viable in part due to incentives, computing power, knowledge/data acquisition, and speed of collection. There are deep incentive problems for planners, programmers, and ordinary participants to complicate efforts at planning and bias data. Most importantly, AI cannot easily or quickly duplicate the signals of relative scarcity that are generated in markets. Some challenges we highlight are pertinent to planning generally, but many others arise from the introduction of an AI-planner.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computation</subject><subject>Computer Science</subject><subject>Computers</subject><subject>Control</subject><subject>Data acquisition</subject><subject>Economic planning</subject><subject>Engineering Economics</subject><subject>Logistics</subject><subject>Machine learning</subject><subject>Marketing</subject><subject>Mechatronics</subject><subject>Methodology of the Social Sciences</subject><subject>Open Forum</subject><subject>Organization</subject><subject>Performing Arts</subject><subject>Robotics</subject><subject>Society</subject><subject>Stagnation</subject><subject>Tacit knowledge</subject><issn>0951-5666</issn><issn>1435-5655</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEURYMoWKt_wNWA6-jL50yWpagVCm50HTKZZEiZZmoyhfrvjY7gztW7i3vug4PQLYF7AlA_ZADCJQZKMRBBGT6doQXhTGAhhThHC1CClCzlJbrKeQcAUjR0gfAqTcEHG8xQhTi5YQi9i9ZVJnaVs2Mc98FWh8HEGGJ_jS68GbK7-b1L9P70-Lbe4O3r88t6tcWWETVh3nHiKGuNB8WkktJbyWvgqhWmJsZL7mxHbWuEYI0nilpBeccc8cB5Qzu2RHfz7iGNH0eXJ70bjymWl5pBzZgqW7S06Nyyacw5Oa8PKexN-tQE9LcWPWvRRYv-0aJPBWIzlEs59i79Tf9DfQHhqmSC</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Gmeiner, Robert</creator><creator>Harper, Mario</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TK</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-7470-6167</orcidid></search><sort><creationdate>20240601</creationdate><title>Artificial intelligence and economic planning</title><author>Gmeiner, Robert ; Harper, Mario</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-4d41e23baf0936966fc647049b5a71af64ecd2cba5538f192c524d3e1f04482d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computation</topic><topic>Computer Science</topic><topic>Computers</topic><topic>Control</topic><topic>Data acquisition</topic><topic>Economic planning</topic><topic>Engineering Economics</topic><topic>Logistics</topic><topic>Machine learning</topic><topic>Marketing</topic><topic>Mechatronics</topic><topic>Methodology of the Social Sciences</topic><topic>Open Forum</topic><topic>Organization</topic><topic>Performing Arts</topic><topic>Robotics</topic><topic>Society</topic><topic>Stagnation</topic><topic>Tacit knowledge</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gmeiner, Robert</creatorcontrib><creatorcontrib>Harper, Mario</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>AI & society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gmeiner, Robert</au><au>Harper, Mario</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence and economic planning</atitle><jtitle>AI & society</jtitle><stitle>AI & Soc</stitle><date>2024-06-01</date><risdate>2024</risdate><volume>39</volume><issue>3</issue><spage>985</spage><epage>1007</epage><pages>985-1007</pages><issn>0951-5666</issn><eissn>1435-5655</eissn><abstract>The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence (AI). We conclude that central planning coupled with AI is still unable to allocate resources with the same efficiency as price signals and market forces through examinations of the technical structure of current AI approaches. AI-driven central planning is not viable in part due to incentives, computing power, knowledge/data acquisition, and speed of collection. There are deep incentive problems for planners, programmers, and ordinary participants to complicate efforts at planning and bias data. Most importantly, AI cannot easily or quickly duplicate the signals of relative scarcity that are generated in markets. Some challenges we highlight are pertinent to planning generally, but many others arise from the introduction of an AI-planner.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00146-022-01523-x</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-7470-6167</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0951-5666 |
ispartof | AI & society, 2024-06, Vol.39 (3), p.985-1007 |
issn | 0951-5666 1435-5655 |
language | eng |
recordid | cdi_proquest_journals_3073390492 |
source | SpringerLink Journals - AutoHoldings |
subjects | Algorithms Artificial Intelligence Computation Computer Science Computers Control Data acquisition Economic planning Engineering Economics Logistics Machine learning Marketing Mechatronics Methodology of the Social Sciences Open Forum Organization Performing Arts Robotics Society Stagnation Tacit knowledge |
title | Artificial intelligence and economic planning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T21%3A25%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20intelligence%20and%20economic%20planning&rft.jtitle=AI%20&%20society&rft.au=Gmeiner,%20Robert&rft.date=2024-06-01&rft.volume=39&rft.issue=3&rft.spage=985&rft.epage=1007&rft.pages=985-1007&rft.issn=0951-5666&rft.eissn=1435-5655&rft_id=info:doi/10.1007/s00146-022-01523-x&rft_dat=%3Cproquest_cross%3E3073390492%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3073390492&rft_id=info:pmid/&rfr_iscdi=true |