Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya

Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or sing...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	de Jong, Charlotte Esmeralda [verfasserIn] Kok, Kasper

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling

Übergeordnetes Werk:	Enthalten in: Environmental modelling & software - Amsterdam [u.a.] : Elsevier Science, 2011, 141
Übergeordnetes Werk:	volume:141

DOI / URN:	10.1016/j.envsoft.2021.105054

Katalog-ID:	ELV006022286

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matchkey_str	article:18736726:2021----::miutiscaeooiasseudrtnigdacnmdligftkhlepretosf
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allfields	10.1016/j.envsoft.2021.105054 doi (DE-627)ELV006022286 (ELSEVIER)S1364-8152(21)00097-9 DE-627 ger DE-627 rda eng 690 004 DE-600 43.03 bkl de Jong, Charlotte Esmeralda verfasserin aut Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required. Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling Kok, Kasper oth Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 141 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:141 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.03 AR 141
spelling	10.1016/j.envsoft.2021.105054 doi (DE-627)ELV006022286 (ELSEVIER)S1364-8152(21)00097-9 DE-627 ger DE-627 rda eng 690 004 DE-600 43.03 bkl de Jong, Charlotte Esmeralda verfasserin aut Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required. Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling Kok, Kasper oth Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 141 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:141 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.03 AR 141
allfields_unstemmed	10.1016/j.envsoft.2021.105054 doi (DE-627)ELV006022286 (ELSEVIER)S1364-8152(21)00097-9 DE-627 ger DE-627 rda eng 690 004 DE-600 43.03 bkl de Jong, Charlotte Esmeralda verfasserin aut Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required. Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling Kok, Kasper oth Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 141 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:141 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.03 AR 141
allfieldsGer	10.1016/j.envsoft.2021.105054 doi (DE-627)ELV006022286 (ELSEVIER)S1364-8152(21)00097-9 DE-627 ger DE-627 rda eng 690 004 DE-600 43.03 bkl de Jong, Charlotte Esmeralda verfasserin aut Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required. Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling Kok, Kasper oth Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 141 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:141 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.03 AR 141
allfieldsSound	10.1016/j.envsoft.2021.105054 doi (DE-627)ELV006022286 (ELSEVIER)S1364-8152(21)00097-9 DE-627 ger DE-627 rda eng 690 004 DE-600 43.03 bkl de Jong, Charlotte Esmeralda verfasserin aut Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required. Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling Kok, Kasper oth Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 141 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:141 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.03 AR 141
language	English
source	Enthalten in Environmental modelling & software 141 volume:141
sourceStr	Enthalten in Environmental modelling & software 141 volume:141
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author	de Jong, Charlotte Esmeralda
spellingShingle	de Jong, Charlotte Esmeralda ddc 690 bkl 43.03 misc Ambiguity misc Social ecological systems misc Fuzzy cognitive maps misc Climate change adaptation misc Participatory modelling Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya
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topic_title	690 004 DE-600 43.03 bkl Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya Ambiguity Social ecological systems Fuzzy cognitive maps Climate change adaptation Participatory modelling
topic	ddc 690 bkl 43.03 misc Ambiguity misc Social ecological systems misc Fuzzy cognitive maps misc Climate change adaptation misc Participatory modelling
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title	Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya
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title_full	Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya
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title_sort	ambiguity in social ecological system understanding: advancing modelling of stakeholder perceptions of climate change adaptation in kenya
title_auth	Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya
abstract	Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required.
abstractGer	Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required.
abstract_unstemmed	Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required.
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title_short	Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya
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up_date	2024-07-06T19:57:23.939Z
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One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. 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Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya

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