{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/369333"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/369333","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"DEliBots : Deliberation Enhancing Bots","abstract":"Deliberation is the dialogue between a group of people that facilitates careful weighing of information, providing adequate speaking opportunities and bridging the differences among participants’ diverse ways of thinking and problem-solving. It is traditionally explored in the field of psychology, where researchers examine the conditions under which groups can make better decisions together. Previous research has shown that a small focus group can outperform wisdom-of-the-crowd. Further, researchers have shown that under some conditions, a group can outperform even the most knowledgeable individual within it, which is also known as the the assembly bonus effect. However, not all groups are equal, and not every group deliberates well together. In this thesis, I introduce the concept of Deliberation Enhancing Bots - DEliBots. The primary objective of a DEliBot is to improve the group's decision-making by providing a framework for good deliberation practices. To achieve this, we leverage conversational probing -- asking questions that stimulate further discussion and innovative thinking within a group setting. A DEliBot is an artificial moderator that models group dynamics and encourages effective deliberation without providing solutions. To achieve this, we address two significant challenges - identifying when to intervene and what should a DEliBot say in order to improve group deliberation. First, regarding timing, we recognise that a moderator should pick a good moment to intervene within a conversation, and intervening too often or too rarely can harm a group's decision-making. Secondly, while uttering a fluent response has already been addressed in dialogue systems research, generating responses that improve group deliberation has not been previously explored. Thus, we introduce a method that models both short-term and long-term improvement in deliberation. We perform a human evaluation study, integrating a DEliBot within a group of people solving a cognitive task. We show that a DEliBot can improve groups' decision-making by asking good questions and encouraging good deliberation practices.","abstract_html":"Deliberation is the dialogue between a group of people that facilitates careful weighing of information, providing adequate speaking opportunities and bridging the differences among participants’ diverse ways of thinking and problem-solving. It is traditionally explored in the field of psychology, where researchers examine the conditions under which groups can make better decisions together. Previous research has shown that a small focus group can outperform wisdom-of-the-crowd. Further, researchers have shown that under some conditions, a group can outperform even the most knowledgeable individual within it, which is also known as the the assembly bonus effect. However, not all groups are equal, and not every group deliberates well together. In this thesis, I introduce the concept of Deliberation Enhancing Bots - DEliBots. The primary objective of a DEliBot is to improve the group's decision-making by providing a framework for good deliberation practices. To achieve this, we leverage conversational probing -- asking questions that stimulate further discussion and innovative thinking within a group setting. A DEliBot is an artificial moderator that models group dynamics and encourages effective deliberation without providing solutions. To achieve this, we address two significant challenges - identifying when to intervene and what should a DEliBot say in order to improve group deliberation. First, regarding timing, we recognise that a moderator should pick a good moment to intervene within a conversation, and intervening too often or too rarely can harm a group's decision-making. Secondly, while uttering a fluent response has already been addressed in dialogue systems research, generating responses that improve group deliberation has not been previously explored. Thus, we introduce a method that models both short-term and long-term improvement in deliberation. We perform a human evaluation study, integrating a DEliBot within a group of people solving a cognitive task. We show that a DEliBot can improve groups' decision-making by asking good questions and encouraging good deliberation practices.","abstract_has_math":false,"creators":["Karadzhov, Georgi"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Vlachos, Andreas","Stafford, Thomas"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-11-15","date_published":"2023-11-15","updated_at":"2026-07-22T22:23:57Z","subjects":["deliberation","dialogue systems","group decision-making","natural language processing"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/10203c38-c9b0-49e5-8eda-6d49a71a77d6/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.109182","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Vlachos, Andreas","Stafford, Thomas"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["EPSRC doctoral training scholarship The Vice-Chancellor's Award"]},{"key":"dc:creator","label":"Author","values":["Karadzhov, Georgi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-11-15"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/369333"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["deliberation","dialogue systems","group decision-making","natural language processing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/10203c38-c9b0-49e5-8eda-6d49a71a77d6/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.109182"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/ab6fce34-944e-45ed-a684-b18478af5e54/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Deliberation is the dialogue between a group of people that facilitates careful weighing of information, providing adequate speaking opportunities and bridging the differences among participants’ diverse ways of thinking and problem-solving. 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A DEliBot is an artificial moderator that models group dynamics and encourages effective deliberation without providing solutions. To achieve this, we address two significant challenges - identifying when to intervene and what should a DEliBot say in order to improve group deliberation. First, regarding timing, we recognise that a moderator should pick a good moment to intervene within a conversation, and intervening too often or too rarely can harm a group's decision-making. Secondly, while uttering a fluent response has already been addressed in dialogue systems research, generating responses that improve group deliberation has not been previously explored. Thus, we introduce a method that models both short-term and long-term improvement in deliberation. We perform a human evaluation study, integrating a DEliBot within a group of people solving a cognitive task. 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A DEliBot is an artificial moderator that models group dynamics and encourages effective deliberation without providing solutions. To achieve this, we address two significant challenges - identifying when to intervene and what should a DEliBot say in order to improve group deliberation. First, regarding timing, we recognise that a moderator should pick a good moment to intervene within a conversation, and intervening too often or too rarely can harm a group's decision-making. Secondly, while uttering a fluent response has already been addressed in dialogue systems research, generating responses that improve group deliberation has not been previously explored. Thus, we introduce a method that models both short-term and long-term improvement in deliberation. We perform a human evaluation study, integrating a DEliBot within a group of people solving a cognitive task. 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