Technical and Social Complexity View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2022-07-31

AUTHORS

Babak Heydari , Paulien Herder

ABSTRACT

In this chapter, we will argue that identifying and analysing the key drivers of complexity – within and outside of systems – is generally more useful than trying to find universal definitions and measures. Focusing on the key drivers enables us to identify and evaluate system-level trade-offs and equip us with leverage points that can enable engineering methods to manage system complexity. We will discuss two of the main drivers of complexity: increased interconnectedness amongst systems constituents (network complexity) and multi-level decision-making (multi-agent complexity). These two forces are natural consequences of advances in information and communication technology, and artificial intelligence on the one hand, and changes in the architecture of socio-technical engineering systems that have given rise to open, multi-sided platform systems. As a natural consequence of focusing on complexity drivers, we argue for a shift in perspective, from complexity reduction to complexity management. Moreover, in most complex socio-technical engineering systems, managing complexity requires adopting a lens of system governance – as opposed to conventional engineering design lens – whose goal is to steer the emergent behaviour of the system through a combination of incentive and architecture design. We will argue that to properly manage complexity, the engineering system and its governance structures need to be designed in an integrated fashion, instead of consecutively. We will further argue that proper integration of AI into engineering systems can play a significant role in managing complexity and effective governance of such systems. More... »

PAGES

221-250

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-81159-4_9

DOI

http://dx.doi.org/10.1007/978-3-030-81159-4_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1149869867


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "College of Engineering and Network Science Institute, Northeastern University, Boston, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.261112.7", 
          "name": [
            "College of Engineering and Network Science Institute, Northeastern University, Boston, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heydari", 
        "givenName": "Babak", 
        "id": "sg:person.07710131673.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07710131673.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Faculty of Applied Sciences, TU Delft, The Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.5292.c", 
          "name": [
            "Faculty of Applied Sciences, TU Delft, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Herder", 
        "givenName": "Paulien", 
        "id": "sg:person.015450214206.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015450214206.18"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2022-07-31", 
    "datePublishedReg": "2022-07-31", 
    "description": "In this chapter, we will argue that identifying and analysing the key drivers of complexity \u2013 within and outside of systems \u2013 is generally more useful than trying to find universal definitions and measures. Focusing on the key drivers enables us to identify and evaluate system-level trade-offs and equip us with leverage points that can enable engineering methods to manage system complexity. We will discuss two of the main drivers of complexity: increased interconnectedness amongst systems constituents (network complexity) and multi-level decision-making (multi-agent complexity). These two forces are natural consequences of advances in information and communication technology, and artificial intelligence on the one hand, and changes in the architecture of socio-technical engineering systems that have given rise to open, multi-sided platform systems. As a natural consequence of focusing on complexity drivers, we argue for a shift in perspective, from complexity reduction to complexity management. Moreover, in most complex socio-technical engineering systems, managing complexity requires adopting a lens of system governance \u2013 as opposed to conventional engineering design lens \u2013 whose goal is to steer the emergent behaviour of the system through a combination of incentive and architecture design. We will argue that to properly manage complexity, the engineering system and its governance structures need to be designed in an integrated fashion, instead of consecutively. We will further argue that proper integration of AI into engineering systems can play a significant role in managing complexity and effective governance of such systems.", 
    "editor": [
      {
        "familyName": "Maier", 
        "givenName": "Anja", 
        "type": "Person"
      }, 
      {
        "familyName": "Oehmen", 
        "givenName": "Josef", 
        "type": "Person"
      }, 
      {
        "familyName": "Vermaas", 
        "givenName": "Pieter E.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-81159-4_9", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-030-81158-7", 
        "978-3-030-81159-4"
      ], 
      "name": "Handbook of Engineering Systems Design", 
      "type": "Book"
    }, 
    "keywords": [
      "engineering systems", 
      "artificial intelligence", 
      "architecture design", 
      "complexity management", 
      "communication technologies", 
      "emergent behavior", 
      "complexity reduction", 
      "complexity drivers", 
      "system complexity", 
      "such systems", 
      "engineering methods", 
      "platform system", 
      "integrated fashion", 
      "complexity", 
      "proper integration", 
      "intelligence", 
      "system", 
      "architecture", 
      "AI", 
      "system constituents", 
      "system governance", 
      "design lens", 
      "technology", 
      "drivers", 
      "information", 
      "integration", 
      "key drivers", 
      "goal", 
      "design", 
      "leverage points", 
      "natural consequence", 
      "definition", 
      "management", 
      "advances", 
      "method", 
      "fashion", 
      "significant role", 
      "point", 
      "hand", 
      "effective governance", 
      "chapter", 
      "social complexity", 
      "perspective", 
      "main drivers", 
      "universal definition", 
      "combination of incentives", 
      "governance", 
      "combination", 
      "measures", 
      "behavior", 
      "interconnectedness", 
      "incentives", 
      "structure", 
      "governance structures", 
      "lens", 
      "consequences", 
      "rise", 
      "reduction", 
      "role", 
      "changes", 
      "shift", 
      "force", 
      "constituents"
    ], 
    "name": "Technical and Social Complexity", 
    "pagination": "221-250", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1149869867"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-81159-4_9"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-81159-4_9", 
      "https://app.dimensions.ai/details/publication/pub.1149869867"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-09-02T16:14", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/chapter/chapter_329.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-81159-4_9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-81159-4_9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-81159-4_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-81159-4_9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-81159-4_9'


 

This table displays all metadata directly associated to this object as RDF triples.

146 TRIPLES      22 PREDICATES      87 URIs      79 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-81159-4_9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:0806
4 schema:author N9039ad948bc94ae885fb4fb59e4610ab
5 schema:datePublished 2022-07-31
6 schema:datePublishedReg 2022-07-31
7 schema:description In this chapter, we will argue that identifying and analysing the key drivers of complexity – within and outside of systems – is generally more useful than trying to find universal definitions and measures. Focusing on the key drivers enables us to identify and evaluate system-level trade-offs and equip us with leverage points that can enable engineering methods to manage system complexity. We will discuss two of the main drivers of complexity: increased interconnectedness amongst systems constituents (network complexity) and multi-level decision-making (multi-agent complexity). These two forces are natural consequences of advances in information and communication technology, and artificial intelligence on the one hand, and changes in the architecture of socio-technical engineering systems that have given rise to open, multi-sided platform systems. As a natural consequence of focusing on complexity drivers, we argue for a shift in perspective, from complexity reduction to complexity management. Moreover, in most complex socio-technical engineering systems, managing complexity requires adopting a lens of system governance – as opposed to conventional engineering design lens – whose goal is to steer the emergent behaviour of the system through a combination of incentive and architecture design. We will argue that to properly manage complexity, the engineering system and its governance structures need to be designed in an integrated fashion, instead of consecutively. We will further argue that proper integration of AI into engineering systems can play a significant role in managing complexity and effective governance of such systems.
8 schema:editor N97913e74f2944004996214e10cecb029
9 schema:genre chapter
10 schema:isAccessibleForFree false
11 schema:isPartOf N90871ba88844412ea42287daaf538b3d
12 schema:keywords AI
13 advances
14 architecture
15 architecture design
16 artificial intelligence
17 behavior
18 changes
19 chapter
20 combination
21 combination of incentives
22 communication technologies
23 complexity
24 complexity drivers
25 complexity management
26 complexity reduction
27 consequences
28 constituents
29 definition
30 design
31 design lens
32 drivers
33 effective governance
34 emergent behavior
35 engineering methods
36 engineering systems
37 fashion
38 force
39 goal
40 governance
41 governance structures
42 hand
43 incentives
44 information
45 integrated fashion
46 integration
47 intelligence
48 interconnectedness
49 key drivers
50 lens
51 leverage points
52 main drivers
53 management
54 measures
55 method
56 natural consequence
57 perspective
58 platform system
59 point
60 proper integration
61 reduction
62 rise
63 role
64 shift
65 significant role
66 social complexity
67 structure
68 such systems
69 system
70 system complexity
71 system constituents
72 system governance
73 technology
74 universal definition
75 schema:name Technical and Social Complexity
76 schema:pagination 221-250
77 schema:productId N7d8b25ff1948442b8c0fcf41d91d34cd
78 Ne3ab054b659749f0a6830c435a672caa
79 schema:publisher N4277444ebc1c4bfc80669205ce7b6839
80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149869867
81 https://doi.org/10.1007/978-3-030-81159-4_9
82 schema:sdDatePublished 2022-09-02T16:14
83 schema:sdLicense https://scigraph.springernature.com/explorer/license/
84 schema:sdPublisher N4a0e5e4edbd7430ea0efb1c98b9d7e06
85 schema:url https://doi.org/10.1007/978-3-030-81159-4_9
86 sgo:license sg:explorer/license/
87 sgo:sdDataset chapters
88 rdf:type schema:Chapter
89 N4277444ebc1c4bfc80669205ce7b6839 schema:name Springer Nature
90 rdf:type schema:Organisation
91 N44df099cf33047739e53e742b3e61c2f schema:familyName Vermaas
92 schema:givenName Pieter E.
93 rdf:type schema:Person
94 N4a0e5e4edbd7430ea0efb1c98b9d7e06 schema:name Springer Nature - SN SciGraph project
95 rdf:type schema:Organization
96 N4ef633987a6f47c898c8e1b895be349a rdf:first sg:person.015450214206.18
97 rdf:rest rdf:nil
98 N5f46e3e1a43c4008bb9fd46e99066fab rdf:first N71483fa0851240e59cafc3fcde7d9297
99 rdf:rest N8d1d00c23cd34e7aacf61e15cbb37f83
100 N71483fa0851240e59cafc3fcde7d9297 schema:familyName Oehmen
101 schema:givenName Josef
102 rdf:type schema:Person
103 N7d8b25ff1948442b8c0fcf41d91d34cd schema:name dimensions_id
104 schema:value pub.1149869867
105 rdf:type schema:PropertyValue
106 N8d1d00c23cd34e7aacf61e15cbb37f83 rdf:first N44df099cf33047739e53e742b3e61c2f
107 rdf:rest rdf:nil
108 N9039ad948bc94ae885fb4fb59e4610ab rdf:first sg:person.07710131673.07
109 rdf:rest N4ef633987a6f47c898c8e1b895be349a
110 N90871ba88844412ea42287daaf538b3d schema:isbn 978-3-030-81158-7
111 978-3-030-81159-4
112 schema:name Handbook of Engineering Systems Design
113 rdf:type schema:Book
114 N97913e74f2944004996214e10cecb029 rdf:first Ne81952e647c84279bd11ef83e6629fe7
115 rdf:rest N5f46e3e1a43c4008bb9fd46e99066fab
116 Ne3ab054b659749f0a6830c435a672caa schema:name doi
117 schema:value 10.1007/978-3-030-81159-4_9
118 rdf:type schema:PropertyValue
119 Ne81952e647c84279bd11ef83e6629fe7 schema:familyName Maier
120 schema:givenName Anja
121 rdf:type schema:Person
122 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
123 schema:name Information and Computing Sciences
124 rdf:type schema:DefinedTerm
125 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
126 schema:name Artificial Intelligence and Image Processing
127 rdf:type schema:DefinedTerm
128 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
129 schema:name Information Systems
130 rdf:type schema:DefinedTerm
131 sg:person.015450214206.18 schema:affiliation grid-institutes:grid.5292.c
132 schema:familyName Herder
133 schema:givenName Paulien
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015450214206.18
135 rdf:type schema:Person
136 sg:person.07710131673.07 schema:affiliation grid-institutes:grid.261112.7
137 schema:familyName Heydari
138 schema:givenName Babak
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07710131673.07
140 rdf:type schema:Person
141 grid-institutes:grid.261112.7 schema:alternateName College of Engineering and Network Science Institute, Northeastern University, Boston, MA, USA
142 schema:name College of Engineering and Network Science Institute, Northeastern University, Boston, MA, USA
143 rdf:type schema:Organization
144 grid-institutes:grid.5292.c schema:alternateName Faculty of Applied Sciences, TU Delft, The Netherlands
145 schema:name Faculty of Applied Sciences, TU Delft, The Netherlands
146 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...