Curated definitions of key terms relevant to collective intelligence, democracy and governance.
We couldn’t list them all, so do get in touch should you wish to contribute.
Aggregation is a key component of any collective intelligence process, as it refers to the process of collecting and combining the knowledge, opinions and expertise from a variety of sources (including citizens, experts, and stakeholders), in order to inform collective decisions and arrive at a solution. The way the aggregation process of individual perspectives and ideas is organized will determine whether those can help groups reach more informed, nuanced, and accurate conclusions than any single individual could have achieved alone.
Key aggregation methods include averaging (i.e. simply taking the average of the individual judgments or ratings) and consensus-based methods. Consensus-based methods involve identifying the options that are preferred by the majority or that best reflect the shared values or interests of the group. This can be done for instance through traditional voting or more sophisticated voting mechanisms (e.g. the Modified Borda count which assigns points to each option based on the preferences of individual voters); collaborative multi-criteria decision analysis (which involves weighing and comparing different criteria or factors that are relevant to a decision, such as cost, effectiveness, and feasibility); consensus building by addressing the concerns and preferences of all group members in order to reach a decision that everyone can support (e.g. through the consent decision-making approach of sociocracy). It often involves open discussion, active listening, and negotiation.
For the best outcomes, aggregation should be considered together with deliberation. Individuals debate in a public forum and potentially revise their judgements in light of deliberation. Once this process is exhausted, a rule is applied to aggregate post-deliberation judgements in order to make a social choice.
Aggregative democracy is a theory of democracy that emphasizes the importance of aggregating the preferences and opinions of individual citizens in order to arrive at collective decisions. In this model, individual citizens are seen as rational actors who make choices based on their own self-interest or values, and these individual preferences are then combined and tallied to arrive at a collective decision. Aggregative democracy typically involves the use of majority rule, in which the preference of the largest group of voters is given priority over the preferences of smaller groups. This approach is often contrasted with deliberative democracy, which emphasizes the importance of dialogue and consensus-building among citizens in order to arrive at decisions that reflect the values and interests of the community as a whole.
Critics of aggregative democracy argue that it can lead to the marginalization of minority groups and overlook the importance of individual rights and liberties. “Political theorists of various persuations are critical of democratic institutional arrangements that rely solely or even primarily on electoral mechanisms, that is, on ways of aggregating individual interests or preferences. They regularly complain that aggregation is, in various ways, inadequate to the task of producing normatively binding political outcomes. They insist that aggregation needs to be supplemented and perhaps entirely supplanted by institutional arrangements that embody and enhance democratic deliberation” (Knight and Johnson 1994, 277). Supporters, on the other hand, argue that it provides a practical and efficient way of making decisions in large and diverse societies. Advocates of aggregative theories of democracy see “the representative as merely a conduit for the transmission of the preferences and interests of constituents” because they believe that “all legitimacy must be anchored in a popular vote” (Heath 2016).
This regulatory approach translates the agile principles of software development in a political setting. Agile governance is an approach to governance that emphasizes flexibility, adaptability, and responsiveness in the face of rapidly changing conditions and challenges. It is based on the principles of agile software development, which emphasizes iterative, collaborative, and customer-centric approaches to creating and delivering software products.
In the context of governance, agile governance involves a shift away from rigid, top-down decision-making processes towards more collaborative, decentralized, and adaptive approaches. It involves empowering individuals and teams at all levels of government to experiment, learn, and adapt quickly in response to changing circumstances, rather than relying on centralized planning and control.
Agile governance is often associated with the use of digital technologies to improve transparency, participation, and collaboration in government decision-making. It also emphasizes the importance of data-driven decision-making and continuous feedback and evaluation to ensure that government policies and programs are meeting the needs of citizens and achieving their intended outcomes.
Critics of agile governance argue that it can lead to a lack of accountability and oversight, and that it may not be suitable for all types of policy and decision-making contexts. However, supporters argue that it can help governments to become more responsive, effective, and efficient in addressing complex and rapidly changing challenges in today’s fast-paced world.
Artificial intelligence (AI) refers to the ability of machines or computer systems to perform tasks that would typically require human intelligence, such as learning, problem-solving, and decision-making. AI systems use algorithms, statistical models, and other techniques to analyze and interpret data, recognize patterns, and make predictions or decisions based on that analysis. Also, “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” (McCarthy 2007).
“The first underpinning concept of modern AI is to consider how a biological brain operates in terms of its basic functioning, how it learns, how it evolves and how it adapts over time. The second point is a need to obtain relatively simple models of the fundamental elements – the building blocks, if you like – of the brain. Third, these building blocks are mimicked by a technological design – possibly a piece of electronic circuitry, possibly a computer program, the aim of which is to simulate the building blocks. The artificial building blocks can then be plugged together and modified in different ways to operate in a brain- like fashion” (Warwick 2012, p. 89).
Some distinguish “strong AI” whereby “machines can think in the same way as humans” from “weak AI”, whereby “machines can demonstrate intelligence but are not necessarily conscious like a human mind (Warwick 2012, p. 178).
“Much of the current discourse on AI is focused on how machine-learning systems help humans make better business decisions.101 Machine learning AI systems are designed to accomplish specific tasks, by accessing and analyzing enormous volumes of data and providing intelligence so that humans can make faster, more efficient, and more effective decisions. The fear is that, as advances in AI are made, systems could engage in “recursive self-improvement” and trigger an intelligence explosion that surpasses human intellect. At this point, humans will no longer be needed to develop, train, and manage various AI applications or interpret results and make decisions” (Metcalf et al. 2019, p. 102).
According to Geoff Mulgan (2018), “The best examples of collective intelligence are best understood as assemblies of multiple elements – different elements co-evolving with their environment.
Assemblies bring together many elements of collective intelligence into a single system.
They show how the world could think on a truly global scale, tracking such things as outbreaks of disease or the state of the world’s environments, and feeding back into action.”
He adds that “Most successful collective intelligences look much more like hybrids, assemblies of multiple elements” (Mulgan 2018, p. 27). Also, “to work well, and serve a whole system, whether that’s within a company or run as a public good, an assembly needs to combine many elements: rich sources of observation and data; models that can make predictions; capacities to interpret and analyze; abilities to create and innovate in response to new problems and opportunities; a structured memory, including of what’s worked in the past; and a link into action and learning that’s aligned with how people really behave.
The test of these elements, when linked up, is then whether they help a whole system think and act more effectively” (Mulgan 2018, p. 28).
Authority bias, also known as the halo effect, refers to a cognitive bias that leads individuals to give undue weight or credibility to the opinions or actions of authority figures, such as experts, leaders, or celebrities, without critically evaluating their claims or behaviors. This bias is based on the assumption that individuals in positions of authority are more knowledgeable, competent, and trustworthy than others, even when there is little evidence to support this assumption.
Authority bias can have significant impacts on decision-making processes in many domains, including politics, business, and healthcare. For example, individuals may be more likely to support policies advocated by political leaders or media figures they admire, without critically evaluating the evidence or potential consequences of those policies.
To avoid authority bias, it is important to critically evaluate the evidence and arguments presented by authority figures, and to seek out alternative perspectives and sources of information before making decisions. This concept is considered one of the so-called social cognitive biases or collective cognitive biases. The Milgram experiment in 1961 was the classic experiment that established its existence. The conclusion of the Milgram experiment was that “Ordinary people are likely to follow orders given by an authority figure, even to the extent of killing an innocent human being. Obedience to authority is ingrained in us all from the way we are brought up. People tend to obey orders from other people if they recognize their authority as morally right and / or legally based. This response to legitimate authority is learned in a variety of situations, for example in the family, school and workplace” (McLeod 2007).
The bandwagon effect is a phenomenon of public opinion whereby people tend to join what they perceive to be existing or expected majorities or dominant positions in society. It has been most intensely discussed with regard to elections and issue attitudesthe rate of uptake of beliefs, ideas, fads and trends increases the more that they have already been adopted by others. As more people come to believe in something, others also “hop on the bandwagon” regardless of the underlying evidence.
The tendency to follow the actions or beliefs of others can occur because individuals directly prefer to conform, or because individuals derive information from others. Both explanations have been used for evidence of conformity in psychological experiments.
The bandwagon effect explains why there are fashion trends. When individuals make rational choices based on the information they receive from others, economists have proposed that information cascades can quickly form in which people decide to ignore their personal information signals and follow the behavior of others. Cascades explain why behavior is fragile—people understand that they are based on very limited information. As a result, fads form easily but are also easily dislodged. Such informational effects have been used to explain political bandwagons.
Bayesian networks, also known as belief networks or probabilistic networks, are a type of probabilistic graphical model that represents the relationships between variables and their probability distributions. They are widely used in artificial intelligence, machine learning, and decision analysis to model complex systems that involve uncertainty, such as medical diagnoses, financial forecasting, and risk analysis. “Learning the structure of a Bayesian network model that represents a domain can reveal insights into its underlying causal structure” (Margaritis 2003).
Behavioural insights for policy-making:
The field of behavioral insights for policy-making, also known as behavioral economics or behavioral science, is a relatively new area of research that draws on insights from psychology, economics, and other social sciences to better understand how people make decisions and to develop policies and interventions that can help to improve those decisions.
The basic premise of behavioral insights is that individuals often do not make decisions based purely on rational calculations, but are influenced by a variety of social, cognitive, and emotional factors. By studying these factors, researchers in the field of behavioral insights seek to identify ways to design policies and interventions that can help people make better decisions, often through subtle changes in the way that information is presented, options are framed, or incentives are structured.
Some examples of behavioral insights interventions include default options, nudges, and social norms. A default option is a pre-set choice that individuals can accept or reject, such as automatically enrolling employees in a retirement savings plan unless they opt out. A nudge is a small change in the choice architecture that encourages people to make a particular decision without restricting their freedom of choice. A social norm is an unwritten rule or expectation that governs behavior, such as the norm of recycling or not littering.
Overall, the field of behavioral insights has shown promise in improving policy outcomes in areas such as healthcare, education, and finance, and is increasingly being used by governments and other organizations around the world to develop more effective and evidence-based policies and interventions.
Behavioral findings provide an alternative view of the human agent. For instance, Shafir (2013, p.1) notes that “Many aspects of decision making that the normative analysis assumes do not matter (such as how the options are described, as long as the same information is given) prove highly consequential behaviorally, and other factors that are normatively assumed to be of great importance (such as whether an intervention will help save 1,000 birds or 10,000 birds) are, instead, intuitively largely ignored. At the most general level, a couple of deep lessons have emerged that are of great potential relevance to policy-makers: the relevance of context and the unavoidability of construal.”
Data defined by its volume, variety and velocity. Volume refers to the size of the dataset, velocity refers to the speed at which data can be accessed and used, and variety refers to the different types of data that are available to collect and analyze in addition to the structured data found in a typical database.
Citizen science encompasses a diverse range of interdisciplinary methods to tap into the collective intelligence of the general public from collecting data to involving the public more broadly in research design, resource management, and decision-making. UNESCO defines it as “The participation of a range of non-scientific stakeholders in the scientific process. At its most inclusive and most innovative, citizen science involves citizen volunteers as partners in the entire scientific process, including determining research themes, questions, methodologies, and means of disseminating results.”
A citizens’ assembly is a body formed from the citizens of a state to deliberate on an issue or issues of local or national importance. The membership of a citizens’ assembly is randomly selected, as in other forms of sortition. Their recommendations are submitted to the policy makers. This governance approach allows people to settle issues by face‐to‐face negotiation among those concerned rather than by electing representatives or relying on secret ballot referenda. It thus follows the logic of a more “unitary democracy” instead of an adversary setting.
Civic tech refers to the use of technology and digital tools to enhance citizen participation, improve government transparency and accountability, and promote social and political change. An effective and easy-to-use civic technology platform enables broad participation. Civic technology combines open civic data, technology, and a new set of collaborative civic technology practices in order to facilitate effective government. What distinguishes civic technology from traditional uses of technology is its reliance on open and voluntary sharing of information, ideas, and initiatives among governments and other stakeholders. This has the potential to change the relationship between government and other sectors. In the best case, this might promote creativity, education, innovation, and learning; remove barriers to participation, knowledge, and services; and build intellectual, social, and human capacities.
Co-creation in policy making refers to a collaborative approach to policy making that involves citizens, businesses, and other stakeholders in the design and implementation of policies, ensuring that they are better aligned with the needs and aspirations of the public
Cognitive biases refer to the systematic errors in thinking and decision-making that humans tend to exhibit, often unconsciously. These biases can distort people’s understanding of events, facts, and other people and lead to non reasonable or inaccurate judgments and behaviors.
Cognitive disinhibition relates to the ability to let go of conventions and other inhibitions to be more imaginative.
Hélène Landemore (2017) defines cognitive diversity as “the variety of mental tools that human beings use to solve problems or make predictions in the world”.
The variation in the way individuals think, process information, and approach problem-solving can lead to groups’ ability to produce more creative and innovative solutions. It encompasses different perspectives, knowledge, experiences, cognitive styles, interpretations, heuristics, and predictive models.
Landemore stresses that “cognitive diversity is generally as important as, and in some contexts more important than, individual ability for the emergence of the phenomenon of collective intelligence.”
Page and Scott have concluded that it is in fact epistemically better to have a larger group of average but cognitively diverse people than a smaller group of very smart but homogeneously thinking individuals.
This notion suggests that the intelligence of a group results as much from the diversity of points of view as from the accuracy of the members of the group’s analyses. Even superficial analysis can contribute to overall better group performance as long as it increases the diversity of viewpoints as the diversity of opinions makes it possible not only to compensate for the shortcomings of each contributor, but also neutralize the mistakes of different participants.
Emile Servan-Schreiber thus concludes that “the enemy of intelligence is not to be found in numbers, but in conformity.”
Cognitive styles refer to the differences in how people habitually think about the world, distinguishing for instance between “verbalizers”, “object visualizers”, and “spatial visualizers”.
Tom Malone, in Superminds (p.38) notes that “When we analyzed the collective intelligence of groups with various mixes of these cognitive styles, we found that the most collectively intelligent groups were those with an intermediate level of cognitive diversity. In other words, groups where the members had very different cognitive styles weren’t as smart, perhaps because they couldn’t communicate effectively with one another. And groups where all the members had the same cognitive style weren’t as smart, either, perhaps because they didn’t have the range of skills needed to do the different tasks.”
Collaboration is a process of working together towards a common goal or objective. It involves sharing knowledge, resources, and expertise to achieve a shared outcome that is greater than what any individual or group could accomplish alone. Collaboration requires trust, mutual respect, and effective communication to ensure that each person or group is contributing effectively towards the shared goal.
Collaboration is essential from a collective intelligence perspective, as it enables individuals or groups to share knowledge, resources, and expertise to arrive at better solutions and decisions. By pooling their collective knowledge and expertise, groups can generate a wider range of ideas and perspectives, leading to more effective problem-solving and decision-making.
Collective efficacy refers to the shared belief among members of a group or community that they have the ability to work together to achieve common goals and solve problems. It is a measure of the collective confidence and sense of agency that exists within a community, and reflects the extent to which individuals feel empowered to take action and make positive changes in their environment.
Social psychologist Albert Bandura’s defines collective efficacy as “a group’s shared belief in its conjoint capabilities to organize and execute the courses of action required to produce given levels of attainments” (Watson et al. 2001, 1057). According to Bandura, collective efficacy is based on four main factors:
Participatory decision-making processes, community organizing, and methods such as Appreciative Inquiry have the potential to increase a group’s sense of collective efficacy.
We define collective intelligence as the capacity of groups to outperform individuals in problem-solving, innovation, prediction, creativity, and other cognitive tasks. (see our Routledge “Handbook of Collective Intelligence for Democracy and Governance”)
This capacity, in turn, can be derived from the various kinds of knowledge listed above: explicit, tacit, centralized, distributed, or embodied.
There are many definitions of collective intelligence. Thomas Malone lists some of the most representatives ones in his Handbook of Collective Intelligence:
G.Mulgan in Big Mind (p.14) reminds us that “The word collective derives from colligere. This joins col, “together,” and once again, legere, “choose.” The collective is who we choose to be with, who we trust to share our lives with. So collective intelligence is in two senses a concept about choice: who we choose to be with and how we choose to act.
The phrase has been used in recent years primarily to refer to groups that combine together online. But it should more logically be used to describe any kind of large-scale intelligence that involves collectives choosing to be, think, and act together. That makes it an ethical as well as technical term, which also ties into our sense of conscience–a term that is now usually understood as individual, but is rooted in the combination of con (with) and scire (to know).”
Collective politicial intelligence:
Collective political intelligence refers to the capacity of a group or society to collectively understand and navigate complex political issues and make informed decisions. It involves the ability of individuals to work together and share information, perspectives, and insights to arrive at a common understanding of complex political problems and identify effective solutions. Collective political intelligence is rooted in the idea that diverse perspectives, experiences, and knowledge can be brought together to form a more complete understanding of complex issues than any one individual could achieve alone. This approach values collaboration, deliberation, and inclusive decision-making processes as key components of effective political action.
According to Eva Sørensen, “The advancement of collective political intelligence relies on politicians that can exercise a particular kind of interactive leadership that thrives on extensive and close dialogue with citizens on the substance of policy, and aims to integrate this activity in the institutional architecture of the policy-making process.” (Sørensen 2020).
Relative to democratic governance, deliberation is the act of thinking about or discussing something and deciding carefully. Habermas (1970) describes deliberation as the “ideal speech situation” and as a moment in which “collective learning is enhanced because individuals are free to communicate openly, completly free from compulsion or distortions of power, and the force of the better argument may prevail.”
Deliberative democracy is a political school of thought and practice that emphasizes the importance of public deliberation and discussion in decision-making processes. It posits that democratic legitimacy is not simply a matter of majority rule, but rather depends on the quality of the deliberation that precedes the decision. In a “deliberative democracy”, the aim is for both elected officials and the general public to use deliberation rather than power-struggle as the basis for their vote. In a deliberative democracy, citizens are indeed encouraged to engage in reasoned and respectful discussion, with the aim of reaching a shared understanding of the issues at hand and arriving at a decision that is in the best interests of the community as a whole.
According to Bohman (1997), “The concept of deliberative democracy is based on the principle that legitimate democracy issues from the public deliberation of citizens. (…) Broadly defined, deliberative democracy refers to the idea that legitimate lawmaking issues from the public deliberation of citizens. As a normative account of legitimacy, deliberative democracy evokes ideals of rational legislation, participatory politics, and civic self-governance. In short, it presents an ideal of political autonomy based on the practical reasoning of citizens” (Bohman 1997). Deliberative democracy methods indeed seek to foster greater participation, inclusion, and transparency in the democratic process, and to promote the values of open-mindedness, mutual respect, and rationality.
“Emergence is a concept in systems theory and philosophy that refers to the way in which complex patterns and behaviors can arise from the interactions between simple components. In the context of emergent intelligence, emergence refers to the way in which intelligent behavior can emerge from the collective behavior of a group of agents, without any centralized control or planning.
Emergent intelligence is based on the idea that intelligent behavior can arise from the interactions between agents, without the need for a centralized intelligence or control. This can happen when agents follow simple rules or behaviors, and their interactions with each other give rise to complex and adaptive behaviors at the group level. For instance, in a swarm of bees, each individual bee follows simple rules (e.g. following pheromone trails or avoiding obstacles). However, the overall behavior of the swarm gives rise to complex and adaptive behaviors such as foraging, nest-building, and defense.
Emergence is important in the study of emergent intelligence because it helps to explain how intelligent behavior can arise from simple rules and behaviors at the individual level. By understanding how emergent behavior arises, it is possible to design systems and algorithms that can harness this phenomenon to create more robust and adaptive forms of intelligence.”
Explicit knowledge refers to knowledge that can be easily articulated, contains identifiable facts, is independent of the individual, is codified, and transferable to others. It is knowledge that is typically written down, documented, or otherwise made available in a formal or systematic way. Examples of explicit knowledge include scientific laws, mathematical formulas, company policies, and procedural manuals.
While explicit knowledge is often easy to communicate and transfer, it may not always be sufficient for solving complex problems or making sound decisions. Tacit knowledge, on the other hand, is often critical for achieving high levels of performance in complex tasks and situations, but it is much more difficult to transfer to others.
Facilitation refers to the process of guiding and supporting a group of people engaged in a deliberative process to ensure that the conversation is productive, inclusive, and respectful. The facilitator’s role is to create a supportive environment in which participants can share their perspectives, engage in respectful dialogue, and work towards a common understanding or decision.
Deliberation benefits from quality facilitation as quality deliberation, according to Prof. James Fishkin, requires at least five ingredients:
Facilitation helps enhance all 5 factors.
Groupthink is a phenomenon whereby individuals conform to the group’s opinions and ideas, often leading to flawed decision-making. Some of the common causes of groupthink include:
1. Strong group cohesiveness: When members of a group feel strongly connected and committed to each other, they may be more likely to conform to the group’s norms and avoid dissenting opinions.
2. Insufficient exposure to external influences: Insularity may develop a sense of invulnerability and overconfidence, leading to a lack of critical evaluation of ideas and alternatives.
3. Directive leadership: When a group leader is directive and imposes its views on the group, it can stifle creativity and encourage conformity.
4. Homogeneity: When a group is composed of individuals who share similar backgrounds, values, and experiences, they may be more likely to agree with each other and avoid dissenting opinions.
5. High stress and anxiety: In high-stress situations, groups may feel pressure to make quick decisions and avoid dissenting opinions to maintain a sense of control and reduce anxiety.
6. The illusion of unanimity: When group members assume that everyone else agrees with the group’s decisions, it can create a false sense of consensus and discourage dissent.
Hybrid intelligence is a concept that refers to the collaboration between humans and artificial intelligence (AI) systems to solve complex problems and make decisions. Hybrid intelligence can provide citizens with access to more comprehensive and accurate information, for example to analyze large amounts of data and provide insights on topics such as public opinion or policy proposals, or by facilitating collaboration and communication between citizens and policymakers. Hybrid intelligence can also enable humans and AI systems to work together to solve complex problems by identifying patterns or trends in data, while humans can provide context and interpretation. Together, humans and AI systems can generate more accurate and comprehensive insights than either could alone. However, there are also potential risks and challenges associated with the use of hybrid intelligence in democratic processes. AI systems could be biased or inaccurate. Also, there may be concerns around privacy.
Inclusion is a key principle both for collective intelligence per se and democracy, as it ensures that all individuals and groups have an equal opportunity to participate in decision-making processes and contribute their knowledge and expertise.
Including diverse perspectives and forms of expertise is a crucial driver of collective intelligence. Beyond opening up thought process to larger numbers of people, this involves actively seeking out and including their diverse perspectives, and creating a culture of respect and inclusivity that encourages all individuals to contribute their knowledge, perspectives and experience.
As for democracy, inclusion means ensuring that all citizens have equal access to information and opportunities to participate in the decision-making process. This includes removing barriers to participation, such as language barriers, physical accessibility, or socioeconomic status. By ensuring that all voices are heard and considered, inclusive decision-making processes thus both foster the emergence of more collectively intelligent solutions, and contribute to the legitimacy of the decisions.
In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and will tend to become closer to the expected value as more trials are performed. In the context of democracy and collective intelligence, the Law of Large Numbers can be an important tool for understanding how group decision-making processes can be improved through the aggregation of diverse perspectives and insights.
Legitimacy refers to the extent to which individuals or groups feel that a decision-making process is fair, just, and appropriate. Legitimacy can be influenced by a range of factors, including the transparency of the decision-making process, the perceived competence and expertise of the decision-makers, and the degree of input and participation that is allowed from different stakeholders.
‘Input legitimacy’ refers to the extent to which individuals or groups are able to contribute their perspectives and insights to a decision-making process. Effective input requires a culture of inclusivity, where all members of the group are valued and encouraged to participate, and where decision-makers are willing to listen and respond to diverse viewpoints.
‘Output legitimacy’ refers to the quality and effectiveness of the decisions that are made through a collective decision-making process. Effective output requires careful consideration of all available information, as well as the integration of diverse perspectives and insights in order to arrive at a well-informed and effective decision.
‘Throughput legitimacy’ refers to the efficiency and effectiveness of the decision-making process itself. This can be influenced by a range of factors, including the availability of resources, the quality of communication and collaboration among group members, and the extent to which decision-making processes are structured and organized.
‘Emotional legitimacy’ refers to the extent to which individuals or groups feel that their emotional needs and concerns are being heard and addressed in the decision-making process. This can be particularly important in situations where decisions may have a significant emotional impact on stakeholders, such as in cases of social justice or environmental policy.
Liquid democracy, delegative democracy, proxy democracy, and smartocracy are all forms of democracy that seek to combine elements of direct and representative democracy.
Liquid democracy is a form of delegative democracy that allows citizens to either directly vote on issues or to delegate their voting power to a trusted representative. In this system, individuals can choose to vote on issues themselves or to delegate their vote to someone they trust, who can then cast a vote on their behalf. This can help to ensure that decision-making processes are more representative of the interests and perspectives of the population as a whole. Liquid democracy is a broad category of either already-existing or proposed popular-control apparatuses. New forms of liquid democracy are enabled by modern tools based notably on blockchain whereby individual A of a society can delegate their power to another individual B – and withdraw such power again at any time.
Proxy democracy is similar to delegative democracy in that it allows citizens to delegate their voting power to a trusted representative. However, in this system, representatives are typically elected through a voting process, rather than being chosen based on trust or expertise.
In a majority rule system, decisions are made by counting the number of votes in favor of a particular option, and selecting the option with the highest number of votes as the preferred course of action. It provides that a majority of an organized group will have the power to make decisions binding upon the whole.
This political principle is often used in formal democratic systems, such as elections or legislative bodies. It is a fundamental principle of representative democracy.
In the context of collective intelligence, majority rule can help to ensure that decisions are representative of the interests and perspectives of a broad range of stakeholders. It may however lead to society disregarding the interests of minorities and thus needs to be complemented by other mechanisms, such as deliberation.
According to Hélène Landemore, it is a key driver of collective intelligence in democracy, in association with inclusion, cognitive diversity, and deliberation.
Motivation and incentives are important factors of collective intelligence, as they can influence the willingness of individuals to participate in decision-making processes, share their knowledge and perspectives, and collaborate with others.
Teresa Amabile, psychologist at Harvard University, determined the effect that a promised reward has on creative thinking. She determined that the expectation of a reward or evaluation, even a positive evaluation, squelches creativity. On the other hand, her studies suggest that people will be most creative when they feel motivated primarily by interest, enjoyment, satisfaction, and the challenge of the work itself – not by external pressures. She warns that many organizations, by placing such emphasis on rewards and evaluation, are inadvertently suppressing creativity. On the other hand, Jacob Eisenberg, professor of business at University College Dublin, and William Thompson, psychologist at Macquarie University, found that experienced musicians improvised more creatively when enticed with cash prizes and publicity. What matters, Eisenberg and Thompson suspect, is the type of people involved in the studies. Amabile’s participants tended to be novices, with no background in art, while Eisenberg’s were veteran musicians, with a least 5 years’ experience. Competition apparently motivates experienced creators but inhibits inexperienced ones. An evolving theory suggests that some combination of intrinsic and extrinsic motivation is ideal.
In the context of democracy, factors such as a sense of civic duty, a desire for social change, or the opportunity to have a voice in decisions that affect one’s life can all be powerful motivators for democratic participation.
In the context of collective intelligence, factors such as recognition, feedback, social influence, and a sense of contribution to a larger goal can all be effective motivators for collaboration and knowledge sharing. Therefore, providing incentives for sharing one’s perspectives, encouraging collaboration and constructive dialogue, and promoting transparency and accountability can all help to create an environment that is conducive to more effective decision-making. However, over-reliance on financial incentives can lead to a focus on short-term goals and undermine intrinsic motivation, while an overemphasis on group consensus can stifle dissent and diversity of opinion.
“In the public sector it is unlikely that organisations will expire if they do not develop new ideas. In the absence of the profit motive it is essential to provide other incentives for individuals and organisations, such as greater recognition of success amongst one’s peers.” (Mulgan, Geoff, and David Albury 2003)
Natural Language Processing (NLP) is a type of artificial intelligence that enables computers to analyze and summarize patterns found in spoken or written word. NLP can be a powerful tool for analyzing and understanding the contributions and opinions of citizens. One application of NLP is sentiment analysis, which involves analyzing the language used by individuals in social media, news articles, and other sources to identify patterns of positive or negative sentiment toward particular issues or topics. This can provide insights into public opinion and can help policymakers and others to better understand the concerns and priorities of citizens. Chatbots and other conversational interfaces can be used to support online deliberation and facilitate discussions among citizens.
Open data refers to data that is made freely available for public use, reuse, and redistribution without any legal or technical restrictions. Open data can be a powerful tool for enhancing transparency, accountability, and citizen participation.
Machines are vital for the processing of Open Data. They are good in imitating human evaluation and repeat processes efficiently, however, there are lacking the ability to associate complex ideas, spot relevance or make creative interlinkages.
By using collective intelligence, the skills of machines can be combined with human skills and the wisdom of the crowd. Thereby data can be found, corrected, expanded, linked interpreted, leading to new ways of re-use. AI techniques (e.g. speech recognition, natural language processing, chatbots or voice bots) can unravel deeper insights from sets of data than traditional statistical techniques.
The Overton window – also known as the window of discourse – refers to the range of ideas tolerated in public discourse. The term is named after Joseph P. Overton, who stated that an idea’s political viability depends mainly on whether it falls within this range, rather than on politicians’ individual preferences.
According to Overton, the window contains the range of policies that a politician can recommend without appearing too extreme to gain or keep public office in the current climate of public opinion.
The Overton Window can be influenced by a variety of factors, including public opinion, media coverage, and the actions of political leaders and interest groups. Over time, the range of ideas and policies that fall within the Overton Window can shift, as new ideas and perspectives gain greater visibility and acceptance in the public sphere.
Participatory democracy is seen as a tool for promoting greater citizen engagement, collective intelligence, and democratic governance, by giving citizens a direct role in the decision-making processes of government. In practice, participatory democracy can take many different forms depending on the context and the needs of the community.
It may involve direct democracy mechanisms such as referenda and citizen initiatives, or it may involve more deliberative forms of democracy such as citizen assemblies and town hall meetings. Whatever the form, participatory democracy seeks to promote greater citizen engagement and involvement in the democratic process, and to ensure that citizens have a meaningful say in the decisions that affect their lives.
Sharon Arnstein (1969) found that most techniques of public participation can be used in ways that do not authentically engage the citizenry. Her Ladder of Participation depicts increasing degrees of citizen empowerment in community planning and decision making activities: (1) Nonparticipation (manipulation and therapy); (2) Tokenism (information, consultation, and placation); and (3) Citizen Power or genuine participation (partnership, delegated power, and citizen control).
Polarization happens when people’s attitudinal agreement is strengthened when further processing the available information in terms of deliberation or debate. Therefore, if a group is in agreement on a certain topic, whether political, religious, cultural or otherwise, they have a tendency to only view and consider information which endorses their already established opinions. People then cluster into like-minded, homogeneous groups, which can be detrimental to democratic deliberation.
The decrease of discussion across lines of perspective generates echo chambers, where people seek out only like-minded viewpoints. Polarization can thus have negative consequences for democracy and collective intelligence by limiting the ability of a society or group to make well-informed decisions and work towards shared goals.
To counteract polarization, it is important to promote civil discourse, encourage open-mindedness and the consideration of diverse perspectives, and to build trust and legitimacy in democratic institutions.
Policy innovation and diffusion refer to the process of developing and spreading new policy ideas or approaches in response to emerging social, economic, or political challenges. Policy innovation involves the development of new policy ideas or approaches that are designed to address emerging challenges or to improve upon existing policies. This can involve the creation of new programs or initiatives, the use of new technologies or data sources, or the adaptation of policies from other jurisdictions or sectors. Policy diffusion, on the other hand, refers to the spread of policy ideas or approaches from one jurisdiction or sector to another. This can occur through a variety of mechanisms, such as intergovernmental cooperation, policy learning networks, or the adoption of best practices. Both can facilitate the development of more effective policies and approaches to governance. They can also promote the sharing of knowledge and expertise among policymakers and stakeholders, and can help to build consensus and collaboration around collective problems.
A key mechanism of policy diffusion is policy learning. By observing the politics of policy adoption and the impact of those policies, policymakers can learn from the experiences of other governments. A second mechanism—economic competition—is often raised in conjunction with learning, and these two mechanisms are viewed, at least implicitly, as the most common processes explaining policy diffusion. A third diffusion mechanism—imitation—has received much less attention in the state politics literature, but arises more frequently in comparative politics.
Policy learning refers to “relatively enduring alterations of thought or behavioural intentions which result from experience and which are concerned with the attainment (or revision) of policy objectives” (Heclo 1974, p.306), i.e. the process of acquiring knowledge and insights from the implementation of policies, and using this knowledge to improve policy design and implementation in the future.
Policy learning can occur at various levels, from individual policymakers to entire organizations or jurisdictions. The goal of policy learning is to improve policy outcomes, increase efficiency, and promote innovation and experimentation in policy design and implementation.
It is important for democracy and collective intelligence because it allows for the development of more effective policies that are based on evidence and data. It can also help to build trust and legitimacy in democratic institutions. Policy learning is therefore an important concept in the theory of change literature. However, it has been difficult to operationalise and measure the concept of learning in general.
Stratified randomization is a method used to ensure that participants in a study are representative of the population being studied. It involves dividing the population into different subgroups based on relevant characteristics (such as age, gender, or location), and then selecting a random sample from each subgroup. Stratified randomization can be used to ensure that different perspectives and voices are represented in decision-making processes. For example, stratified randomization can be used in the recruitment of a sample of citizens for a public consultation on a policy issue, or in the evaluation of social programs and policies. This can help to ensure that the results are more generalizable to the broader population and can inform policy and programmatic decisions that are more responsive to the needs of different subgroups.
Swarm intelligence arises from distributed, self organized decision making. Swarm intelligence refers to the collective behavior of decentralized, self-organized systems, typically seen in the natural world among social insects like ants, bees, and termites. In these biological swarms, individual agents follow simple rules and interact with one another to achieve complex, coordinated outcomes at the group level. Similarly, by harnessing the power of many individuals who are able to contribute to the decision-making process, a swarm of participants can work together to find optimal solutions to complex problems. One example of the application of swarm intelligence in collective decision-making is the concept of “crowdsourcing.” Another example is the use of algorithms that mimic the behavior of swarms to optimize decision-making. These algorithms can be used to improve traffic flow, optimize logistics, and predict stock market trends.
Animals, like algorithms, can engage in forms of swarm intelligence – i.e., they can collectively gather pieces of information and combine them through social interaction. Humans, on the other hand, are among those species that are particularly good at developing collaborative and imaginative capacity.
Artificial Swarm Intelligence (ASI) is a collaboration technology that enables groups of people to answer questions, make predictions, express opinions, and reach decisions as a unified emergent intelligence by tracking group members as they signal their intent toward choice alternatives. ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions.
“The emergent decision-making process found in honey bee swarms provides a powerful analog for how human brains arrive at complex decisions and has informed the development of ASI, which enables groups of networked humans to function as a unified intelligence and to make complex and nuanced decisions about known unknowns that draw from the explicit and tacit knowledge of all group members” (Metcalf et al 2019, p. 86).
Tacit knowledge refers to ways of knowing which are not easily expressed in language, i.e. knowledge that resides in individuals, and cannot be easily externalized, as it is shaped by cognitions, feelings and emotions. In other words, “we can know more than we can tell”. It is knowledge that is typically gained through personal experience, practice, and observation, and is often deeply rooted in an individual’s cognitive, technical, and social skills. Examples of tacit knowledge include knowing how to ride a bike, play a musical instrument, or navigate complex social situations. Both explicit and tacit knowledge are important for individual and collective intelligence. An awareness of the importance of tactic knowledge can help decision-makers to leverage the diverse experiences and expertise of stakeholders to promote more effective and inclusive decision-making.
A wicked problem is a complex and multifaceted problem that is difficult to define, has multiple causes and stakeholders, and does not have a clear or simple solution. Wicked problems are “complex, ever changing societal and organisational planning problems that you haven’t been able to treat with much success, because they won’t keep still. They’re messy, devious, and they fight back when you try to deal with them” (Ritchey 2013, 1).
Wicked problems are characterized by high levels of uncertainty, ambiguity, and disagreement among stakeholders about the nature of the problem and the appropriate solutions. Addressing wicked problems requires a range of skills and approaches, including systems thinking, collaborative problem-solving, and adaptive leadership.
Wisdom of crowds is the idea that large groups of people are collectively smarter than most – or even all – individuals on problem-solving, decision making, innovating and predicting. The wisdom of crowds concept was popularized by James Surowiecki in his 2004 book, The Wisdom of Crowds, which shows how large groups have made superior decisions in pop culture, psychology, biology, behavioral economics, and other fields.
This phenomenon has been observed in various contexts, from guessing the weight of a cow at a county fair to predicting the outcomes of political elections. Surowiecki distinguishes between four types of wisdom of crowds, according to the task that is being crowdsourced: information generation (eg wiki-type forms of collaboration); service co-production (production of services); creation; and policy making. Policy-making types of crowdsourcing are meant primarily to elicit ideas and skills for the design of policies in areas as diverse as anti-corruption, urban planning, transportation, and constitutional reforms. Wise crowd judgment relies on a large set of diverse and independent opinions in which random errors cancel out to reveal underlying information (Galton 1907; Surowiecki 2005).
The wisdom-of-the-crowd effect requires motivating large numbers of people with differing opinions to contribute to the system.
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