Interactive genetic algorithm for user‐centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple field...

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Published in:	Water resources research Vol. 53; no. 5; pp. 4303 - 4326
Main Authors:	Piemonti, Adriana Debora, Babbar‐Sebens, Meghna, Mukhopadhyay, Snehasis, Kleinberg, Austin
Format:	Journal Article
Language:	English
Published:	Washington John Wiley & Sons, Inc 01.05.2017
Subjects:	Algorithms Analogies Artificial intelligence Conservation conservation planning Conservation practices Data processing Decision support systems Design Design analysis Design engineering Feedback Fields Genetic algorithms Human behavior Human performance humans human‐computer interaction Hydrologic models Hydrology Immunoglobulin A Interactions interactive genetic algorithm Interfaces Mathematical models Optimization Search methods Search process Searching stakeholders User behavior water Water resources Water resources planning watershed Watersheds
ISSN:	0043-1397, 1944-7973
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Abstract	Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives. Key Points Observational experiments with an online interactive genetic algorithm and real humans were conducted for conservation planning User behavior on the web interface was used to identify the quality of user‐provided data guiding the IGA Similarities and dissimilarities were found in the objective space of plans generated by participants with varying interests
AbstractList	Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives. Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives. Key Points Observational experiments with an online interactive genetic algorithm and real humans were conducted for conservation planning User behavior on the web interface was used to identify the quality of user‐provided data guiding the IGA Similarities and dissimilarities were found in the objective space of plans generated by participants with varying interests Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants ( surrogates and stakeholders ) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders , in comparison to their surrogates , were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives. Observational experiments with an online interactive genetic algorithm and real humans were conducted for conservation planning User behavior on the web interface was used to identify the quality of user‐provided data guiding the IGA Similarities and dissimilarities were found in the objective space of plans generated by participants with varying interests
Author	Babbar‐Sebens, Meghna Mukhopadhyay, Snehasis Kleinberg, Austin Piemonti, Adriana Debora
Author_xml	– sequence: 1 givenname: Adriana Debora surname: Piemonti fullname: Piemonti, Adriana Debora organization: Oregon State University – sequence: 2 givenname: Meghna orcidid: 0000-0001-8140-563X surname: Babbar‐Sebens fullname: Babbar‐Sebens, Meghna email: meghna@oregonstate.edu organization: Oregon State University – sequence: 3 givenname: Snehasis surname: Mukhopadhyay fullname: Mukhopadhyay, Snehasis organization: Indiana University‐Purdue University Indianapolis – sequence: 4 givenname: Austin surname: Kleinberg fullname: Kleinberg, Austin organization: Oregon State University
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Snippet	Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and...
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SubjectTerms	Algorithms Analogies Artificial intelligence Conservation conservation planning Conservation practices Data processing Decision support systems Design Design analysis Design engineering Feedback Fields Genetic algorithms Human behavior Human performance humans human‐computer interaction Hydrologic models Hydrology Immunoglobulin A Interactions interactive genetic algorithm Interfaces Mathematical models Optimization Search methods Search process Searching stakeholders User behavior water Water resources Water resources planning watershed Watersheds
Title	Interactive genetic algorithm for user‐centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior
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