MetaModel for New Social Choice Systems
Author’s note: This proposal is a lightly edited copy of my entry for the 2018 New Shape Prize, a worldwide contest aimed at discovering new ideas that could improve global governance and reduce societal risks. My proposal was not selected to be a finalist and I am publishing it here in hopes that it might stimulate a wider global conversation about the suitability of current governance systems and how we might go about developing ones that better reduce risks, solve important problems, and serve the common good.
I define governance systems broadly to include electoral and legislative systems proper, but also economic/financial and legal/justice systems, and more. I view all as one large “social choice” or “decision-making” system—the rules and mechanisms by which a community or society self-organizes to direct group activity.
A social choice system is a type of technology. Like others, it is subject to innovation, including disruptive innovation. The public already welcomes, even expects bold advances in a wide range of technologies. Why not also in the designs of governance systems?
A core concept for developing new and improved systems is engage global, test local, spread viral. Engage global means to engage professional communities and grassroots groups in a focused, worldwide R&D effort.
Test local means to conduct scientific testing of new systems at the club level, using volunteers. In this way, trials are relatively affordable and low risk, and they require participation by only a small percentage of a local population. Further, no local or national legislative action would be necessary to start a trial in most areas.
Spread viral means that systems expand and spread to new locations (to new clubs) based on demonstrated merits. That is, participation remains voluntarily. The benefits of improved systems could be massive, however, and if so, participation could grow rapidly. For example, a published computer simulation of a prototype local economic system illustrates how a small US county could eliminate poverty, nearly eliminate unemployment, and more than double median family income, all while bringing deeper democracy to local economic and financial choices.
This is the only proposal I am aware of that positions the global academic community, and science and technology sectors, at the center of developing, testing, monitoring, and promoting new governance systems for local implementation. It stands to reason that any form of evidence-based governance would necessarily require the participation of science and technology professionals, as well as academics from a wide range of fields. Unfortunately, and in spite of the urgent need for R&D on new systems (current ones have us on a trajectory leading uncomfortably close to mass extinction), there are currently no academic or science and technology programs in the world that focus on the aims of this proposal. It is my hope that this will soon change, and my fear that it will not.
As per the contest rules, the proposal is organized in three parts: Abstract, Description of the Model, and Motivation. References follow.
This proposal describes a “metamodel,” a model for building, testing, implementing, and monitoring other models. Those end models are governance systems, broadly viewed as problem-solving, or social choice systems. The purpose of the metamodel is to employ the scientific method, and engage the global science, engineering, and technology communities, among others, in developing defensible answers to two simple, powerful, but largely unexplored questions:
1. Out of all conceivable designs for social choice systems, which systems have greatest potential to minimize systemic risk and maximize collective wellbeing at multiple scales — local, regional, and global?
2. How is system quality best measured and monitored?
In this proposal, governance is viewed as the systems by which a society (or any group) solves its problems, organizes activities, and motivates behavior. In this view, governance spans political-legislative-electoral, economic-monetary-financial, legal-justice systems, and more. The term social choice system is used to capture this broad perspective; it encompasses all mechanics and rules of a group problem-solving process that are amenable to human design and innovation — that is to say, all the components that can potentially be changed by participants.
Not all social choice system designs are functional or sustainable. A successful social choice system would include mechanisms to: (1) sense and store information; (2) assess status, select potential actions, and predict outcomes for actions; (3) evaluate predicted outcomes; and (4) make decisions based on evaluations. Thus, a social choice system not only includes core elements of typical political, economic, and related systems, but also survey programs to collect social and environmental data, predictive models to forecast economic and public health impacts of proposed actions, and more.
The ability of a society to address and solve problems depends on the quality of its social choice system. A society that employs a suboptimal social choice system is more likely to fail at solving problems and meeting the core needs of its members. It is also more likely to misdirect energies toward solving the problems of a powerful minority, rather than those that pertain to the common good.
The proposed Social Choice System Metamodel begins with program development, which includes organizational and staff development. On the technical side, this phase includes development of initial standards for model assessment, data use, and reporting of results. It also includes initial development of model quality (fitness) metrics, standard datasets for use in computational testing, and computational models of relationships between socioeconomic, environmental, public health, and other factors related to collective wellbeing.
The next four phases, in sequence, are initial computational assessment of proposed systems, advanced computational assessment, field testing of highly promising systems, and implementation, monitoring, and networking of systems that pass field testing. Given that the program represents a new frontier in applied science, these phases could occur iteratively, with each round exhibiting a greater degree of sophistication.
The models that undergo computational assessment, field testing, and implementation would meet certain criteria. In overview, they engage the public at the local, community level, via special not-for-profit civic clubs in which membership (participation) is voluntary. Each club operates and manages its own system. Participants are not charged a fee to join and each club is non-discriminatory and transparent. Clubs organize only where legal, but they would be legal in large portions of the globe. Finally, clubs and the systems they implement coexist and operate in parallel with existing economic, political, and legal systems. By design, clubs are democratic in the sense that all members can participate in and have meaningful impacts on the group problem-solving process.
In time, the program would focus on social choice systems for networks of clubs, in addition to those for single clubs. While single clubs would have local impact, networks of clubs would have regional and global impact. This proposal argues that the civic club model is preferred over other approaches due to its lower cost and risk, greater capacity for parallel testing, and other reasons to be discussed.
There is good reason to believe that a scientific program focused on discovery and testing of new social choice system designs would bear abundant fruit. With advances in science and technology, including advances in public health, statistics, computer science, complex system science, computational sociology, ecology, information theory, cognitive science, and evolutionary biology, we have the opportunity, perhaps for the first time, to design social choice systems whose fitness — ability to solve problems, minimize systemic risk, and maximize collective wellbeing — is scientifically quantifiable and defensible. Moreover, given suitable fitness metrics, and system monitoring, one can expect that new systems would continue to improve over time.
The Social Choice System Metamodel would almost certainly produce a collection of designs that are fundamentally different from, and more beneficial than, current social choice systems. A prototype example of a new social choice system is the Local Economic, Direct Democracy Association (LEDDA) framework. It includes eight components that span a variety of socioeconomic, organizational, and decision-making topics, and is based on a civic club model.
To give some idea of potential benefits that new systems might offer, a published simulation study of currency flows in an idealized LEDDA, located in a mid-sized US county, illustrates how poverty and income inequality are eliminated, full employment achieved, and median family income more than doubled. All participating families see income gains, regardless of work status. By simulation end, 90 percent of local families join the LEDDA because doing so increases their income. Participants democratically choose how billions in annual currency flow are used, and as such are empowered to meet their needs and to address the problems and challenges that they deem important.
2. Description of the model
This proposal, the Social Choice System Metamodel, describes a scientific program aimed at engaging the global science, engineering, and technology communities, among others, in discovering, testing, and implementing new models or designs for governance systems, viewed broadly as problem-solving, or social choice systems. The program’s mission is to develop scientifically defensible answers to two powerful but largely unexplored questions:
1. Out of all conceivable designs for social choice systems, which systems have greatest potential to minimize systemic risk and maximize collective wellbeing at multiple scales — local, regional, and global?
2. How is system quality best measured and monitored?
In this proposal, governance is viewed as the systems by which a society — a community, large or small — solves its problems, meets its needs, and addresses its challenges. As such, governance also includes the systems by which a society organizes group activities and motivates behavior. Thus, governance spans political-legislative-electoral, economic-monetary-financial, legal-justice systems, and more.
It is well understood that a legislative system, for example, is part of governance. Less understood is that a society also solves problems through economic systems, broadly defined, and so these too are a part of governance. A society uses an economic system to help decide what products to produce, what resources to use, where products are sold, how incomes are distributed, what wastes are created, and who holds decision-making power, among other things. Indeed, decisions made within an economic system can sometimes have greater impacts on society and the environment than those made within a legislative system.
This is not to say that current economic, legislative, and related systems are optimal, or even functional. Economic systems, for example, are usually not conceived of as problem-solving systems, and not designed to function as such. Moreover, when economic systems are used to solve problems, too often the problems addressed are those of the wealthy or other powerful groups, not those of the general population.
Over the past few centuries, the evolution of governance systems has been outpaced by the rising difficulty of problems that must be solved. Problems tend to grow more difficult as a population grows in size and as technology expands — more harm can be done to more people and the environment. To solve or successfully address problems that grow difficult, greater cooperation is required, as well as deeper understanding of conditions and causes. Additional views must be taken into account. Greater transparency is needed to maintain trust. In these and other ways, governance systems must improve to keep pace with challenges. This has not occurred, as evidenced by the host of deadly serious and seemingly intractable problems that societies now face.
Given that the designs of current systems were largely developed prior to modern scientific and technological advances, there is good reason to believe that a scientific program focused on development and implementation of improved designs would bear abundant fruit. Humans now have the opportunity, perhaps for the first time, to consciously design governance systems by a defensible science and engineering process, such that new systems would excel at minimizing systemic risk and at maximizing collective wellbeing (physical, mental, social, environmental, and more).
Social Choice Systems
I have used the term social choice system to capture a broad definition of governance as problem-solving [1,2]. A social choice system is the set of all mechanics and rules used in a group problem-solving process that are amenable to human design and innovation — meaning that they can, in potential, be changed by participants. A generic group problem-solving process is illustrated in Figure 1, where the steps that pertain to social choice system components are indicated.
An example of a mechanism not amenable to design, and so not a component of a social choice system, is the core needs of a group (center of the figure). As will be discussed, these might include needs for affection, creativity, sustenance, and so on. Core needs are a given, stemming from eons of evolution. One could think of them as inputs to the problem-solving process. A successful social choice system would recognize core needs as input and provide mechanics for conveying and using their content.
The Act step (A) in Figure 1 is not considered here as part of a social choice system, but it could be. A group makes decisions, and some decisions would focus on how to orchestrate or implement actions. In this proposal, actions are considered as following directly from decisions.
Depending on design, a social choice system may or may not be functional, and may or may not excel at solving problems. A successful (and therefore sustainable) social choice system includes mechanisms to: (1) sense and store information — S (sense) in the figure; (2) assess status, select potential actions, and predict outcomes for actions — P (predict) in the figure; (3) evaluate predicted outcomes — E (evaluate) in the figure; and (4) make decisions based on evaluations — D (decide) in the figure.
Actions (A) naturally follow decisions (D), and impacts (I) result from actions. If the problem-solving process cycles, as suggested by the inner ring — if sensing of impacts completes the feedback loop — then learning can occur. As such, the problem-solving process and the learning process are nearly one. A successful social choice system is a learning system.
The process illustrated in Figure 1 is similar to that used by individuals to solve problems and learn. Group problem solving is an extension of problem solving at the individual level. Indeed, as will be discussed, a social group can be usefully viewed as a superorganism — an organism comprised of interacting individuals. As such, it can think, solve, act, and learn, not unlike an individual.
In short, a successful social choice system identifies problems that are important to collective wellbeing and solves or adequately addresses them in a timely manner . If it fails to do so in one attempt, it learns from experience and tries again until success is achieved. Moreover, it motivates behavior and organizes group activity in such a way that problem-solving capacity is adequate, if not maximal.
As will be discussed, this proposal focuses on testing and implementation of social choice systems at the local, community level, via a networked club model in which participation is voluntary.
Social Choice Systems as Social Computation
It is helpful to consider the problem-solving (and learning) process of Figure 1 as a form of social computation . Information is gathered, stored, and assessed. Additional computation occurs as predictions are made for the outcomes of potential actions. Still more computation occurs as predicted outcomes are evaluated, and as the results of evaluations are incorporated into decision making.
In all these steps, the movement or transformation of information plays a role. Thus, we can speak of the magnitude of information flow (large or small), storage (fast access or slow), quality (actionable or noisy), bandwidth (wide or narrow), distribution (concentrated or dispersed), and content (relevant or irrelevant). As such, concepts from computer science, sociology, physics, and other fields can be helpful in understanding how social choice systems go wrong and how they might be improved.
In particular, it is useful to view a social choice system as a distributed computing network, with each person serving as a processing and sensing node. An individual can report and assess conditions, and can reason to make predictions, for example.
Thus, a club or society can expand problem-solving capacity by improving all aspects of social computation. For example, via its design and/or funding choices, a club could improve the quality of education, increase access to education, and nurture critical-thinking skills. It could decentralize decision-making power, thus engaging more individuals in the problem-solving process. It could improve how information flows and is distributed. One way to do this would be to expand scientific programs that by nature increase the flow of information and knowledge. The flow of disinformation could also be addressed, perhaps by removing the economic rewards for generating disinformation. Finally, a club could increase computation through use of tools that extend natural human abilities. Since assessment and prediction are key components of learning and problem solving, the potential to use artificial intelligence for the benefit of society is apparent.
Social computation could be improved in other ways, some of which might not be obvious at first glance. For example, to engage large groups in the problem-solving process, a social choice system should be designed such that each individual feels that her contributions are helpful, and that all the information she wishes to offer is accepted and used. Compare this ideal with a system of representative democracy, for example, where every few years a person has opportunity to essentially cast a yes/no vote for an incumbent. Such a voting process incorporates very little of the rich information that a person might have to offer.
The presentation of information is also important. In some businesses or agencies, a goal is to achieve what is called “situational awareness.” Think of a NASA control room, for example, where scientists and staff can watch graphs and other images and presentations of data evolve in real time as a new rocket is launched into space. Having access to rich, expressive, timely, and well-presented information is important to the problem-solving process.
Indeed, a successful social choice system would gather information in real time, or as frequently as practical, on a wide variety of topics that are pertinent to system function and collective wellbeing. These could include for example trade with other areas, energy flows, waste flows, currency flows, public health conditions, and human resource use and needs. Imagine each club with its own type of situational awareness dashboard, where participants can not only monitor conditions as they change, but also see where predictions suggest the future is headed. Likewise, a display of predicted outcomes could be helpful when assessing potential actions on some matter.
Over the coming decades, as the technical capacity of civilization continues to expand, it will become increasingly possible for clubs to achieve a high degree of situational awareness so that they might better understand what is happening today, and how their current choices and actions might affect conditions tomorrow.
Fitness Metrics for Social Choice Systems
Given the preceding discussions, the fitness or quality of a social choice system can be assessed by recognizing two distinct components. The proximal component is problem-solving capacity, and the distal component is the degree of collective wellbeing, current and anticipated, that problem-solving activity produces. One could figuratively understand the proximal and distal components as how well car is built, versus how well it is driven.
Much work remains to develop scientifically defensible measures and metrics of social choice system fitness. However, a substantial body of research already exists on some subtopics that can be drawn upon. For example, work has been done on measures for assessing wisdom, trust, public health, environmental quality, and problem-solving abilities in populations. Census, labor, and economic agencies have deep experience in measuring socioeconomic and demographic indicators. The United Nations has also been active in developing indicators . There is also some work on developing metrics, or summary indexes, for public health and wellbeing. Examples include the Human Development Index and the Index of Economic Well-Being .
In developing fitness metrics for social choice systems, aspects of social computation could be considered. This might involve various measures of information flow including distribution, volume, and quality, as well as system transparency. The distribution of decision-making power could be estimated or measured.
Computational capacity could be estimated through simulation or by theoretical means. Even predictive accuracy could be measured. Did family income, or disease rates or crime, rise or fall according to predictions? Critical thinking skills within a population could be measured.
System robustness and resilience to shock could be estimated by theoretical means or by simulation. So too could more technical aspects of complexity. For example, it might be possible to measure the distance of a system from self-organized criticality [7,8]. For biological systems such as the human brain, it appears that optimal performance occurs when the system is near a critical state (essentially, when stability versus agility, or use of old versus new information, is in optimal balance). Beyond this, it may be possible to incorporate some theoretical measures of decision making, including ideas from social choice theory .
Numerous subjective measures of wellbeing exist. In assessing the quality of social choice systems, however, it is preferable to have fitness metrics that are as objective as possible. This includes theoretical measures and those that are obtained via simulation, as these would allow estimation of fitness long before a system is ever implemented. Objective measures allow meaningful comparisons to be made between systems.
Other measures and metrics of fitness could focus on systemic risk. These might address system failure (for example, through abuse of power or the collapse of currency flows) or environmental collapse. They might also address rates of death, disease, or social unrest. Some work has been done on identifying dynamic patterns within ecological systems that could serve as early warning signs for potential collapse or the traversal of tipping points that portend radical change . Note that systemic risk can be understood as pertaining to a local system and its environment, to a network of implemented systems and its environment, or to a national or global society and their environments.
Beyond assessments already mentioned, one might expect that systemic risk is reduced to the degree that problem-solving capacity and collective wellbeing increase. Thus, it also might be possible to incorporate metrics for these as proxies for systemic risk.
A Prototype Social Choice System
The program would almost certainly produce a collection of social choice system designs that are fundamentally different from, and more fit than, current social choice systems. To give some idea of possibilities, the Local Economic Direct Democracy Association (LEDDA) framework can serve as a prototype . It is a sophisticated system consisting of eight components that span a variety of economic, legislative, justice, and other topics. And it is based on a civic club model.
The framework is in early stage development, but an initial agent-based simulation study has been published . That study is illustrative rather than predictive. It examines currency flows and income changes in an idealized Token Exchange System (TES), a component of the LEDDA framework. The token is a local, community currency that circulates in parallel with a national currency (the dollar, in the study). In the study, the purchasing power of a token is assumed equal to that of the dollar. The study used US Census and other data for Lane County, Oregon, and simulated an adult population of 100,000.
A LEDDA component central to the TES is a bi-currency financial system called the Crowd-Based Financial System (CBFS). Member organizations can request CBFS funding; nonprofits can request funding via loans and donations and for-profits can request funding via loans and subsidies. The CBFS provides funding as a mix of dollars and tokens. Loans are issued interest free.
Individual club members contribute a portion of their gross income, in dollars and tokens, to the CBFS. Some contributions go to the nurture arm, which provides income for members who are not part of the workforce or are unemployed. Each participant decides how the remainder of his or her contributions will be used. For example, at a given point in time a member might choose to help fund a school, a factory, construction of a new fire station, and/or a local health clinic. All CBFS transactions are transparent.
The simulation illustrates how family incomes for members rise over time in a predetermined fashion. At inception, the family income target starts near the equivalent of a minimum wage. In each year, member organizations pay wages at or above the target. By the end of the 28-year simulation the family income target rises to about 110,000 tokens and dollars, roughly equivalent to the starting 90th percentile of family income. This is post-CBFS income, after contributions to the CBFS have been made. All member families, regardless of work status, receive an income of at least the income target.
For the sake of simplicity, it is assumed that incomes do not change over time for those who are not members. Further, people join the LEDDA only if doing so increases their income. As such, by the end of the simulation, 90 percent of the local population has joined. Median family income more than doubles compared to starting values.
Families that earned above the 90th percentile of starting income did not join the LEDDA. In practice they could join, and would annually receive a small fixed incentive paid in tokens if they did. No one who joins is asked to forfeit income. All families that join receive an income gain; the incentive is offered to any family that earns more than the current income target.
One reason a LEDDA is designed to equalize incomes over time (and why participants would want to do so) is that money, both national currency and tokens, is used as a bona fide voting instrument. In all modern economies money already functions as a voting instrument; the more money one has, the more power one has over others. Income inequality translates to inequality of decision-making power. The LEDDA framework makes this voting function explicit, transparent, and fair.
The LEDDA framework includes a novel socially responsible business model, called a Principled Business. It is somewhat of a cross between a nonprofit and for-profit model. A Principled Business has a social mission, is transparent, pays wages consistent with (no greater than the final expected) income target, and follows other rules of the LEDDA. For example, Principled Businesses participate in a patent pool of intellectual property that is designed to increase the flow of ideas and inventions among members. The pool could span multiple LEDDA clubs, via cooperative agreements.
In the long run, local families that earn incomes above the income target are likely to see their incomes fall toward the target. LEDDA members (who may be 90 percent of the local population at club maturity) understand that it is in their best interest to support, through patronage and CBFS funding, Principled Businesses as well as member nonprofits and for-profits that act similar to principled businesses. Thus, local businesses that are not principled businesses or that do not act similarly would not benefit as much from the funding and patronage offered by members.
Further, such businesses would not benefit from the intellectual property pool, and would find it more difficult to recruit staff. To compete with Principled Businesses and similar-acting organizations, they would have to offer jobs that are as meaningful and that pay at least the income target. Without low-income employees to support the wages of high-income employees, some standard business models would falter.
By the end of the simulation, billions in currency flow through the CBFS annually. Thus, members have the means to fund a wide variety of organizations and projects that they deem helpful. This could include schools, research programs, hospitals, arts programs, environmental restoration programs, small farms and other small businesses, community gardens, and so on.
Of course, a LEDDA could also fund frivolous or even harmful programs, but transparency and monitoring and prediction of wellbeing status would make the dangers apparent. Moreover, assuming that a LEDDA is part of a network of clubs, it might risk breaking network rules and thus losing network benefits.
If they desired, members could fund organizations that provide products or services at no or low cost. For example, they could fund a no-fee health-care system or a tuition-free college. They might cooperate with other clubs in this regard.
A LEDDA should be fairly immune to job loss caused by the expansion of artificial intelligence and robotics. If substantial job losses were to occur in some occupation, a LEDDA could choose to fund different jobs. Indeed, a LEDDA could choose to create a relatively large nonprofit sector and a relatively small for-profit sector if it wished.
To carry this to an unrealistic extreme, if robots did all the work, a LEDDA could fund jobs in the arts, gardening, or other enjoyable endeavors. In theory, and assuming that a trade balance can be achieved with other clubs and regions, the circulation of currency in a LEDDA would remain stable under a wide variety of conditions. By design it would be resilient and robust.
Money is not the only voting instrument available to members. An equally important instrument is formal voting in the Collaborative Governance System, another LEDDA component . This is a system of online direct democracy that incorporates ideas from Athenian democracy, liquid democracy , and other sources. Simulation studies of voting in the Collaborative Governance System have not yet been conducted.
The Social Choice System Metamodel
As envisioned, the Social Choice System Metamodel is an applied science program organized as an independent or semi-independent, not-for-profit, non-governmental organization. That organization could be part of an already established organization, such a university or the Global Challenges Foundation, Stockholm Resilience Centre, or the Santa Fe Institute, if any are interested in hosting the program.
Alternatively, the program could develop as a new organization, managed by experienced administrators under the guidance of a board of directors and science advisory boards selected largely from diverse fields within academia, science, technology, and engineering. Additional boards and committees could engage public, government, and other interested groups.
Regardless of how it organizes, the program would seek partnership and/or coordination with a variety of academic institutions and the United Nations, as well as with a variety of civil society and community organizations across the globe.
The program is intended to be reasonably lightweight and nimble, with minimal bureaucratic inertia. Its size would depend on funding. Much could be done on a shoestring budget and tiny staff, but more could be done, faster and with more thoroughness, on a budget and staff that are appropriate for the program’s mission. Three levels of funding might be considered: shoestring, light, and full.
Assuming full funding, staff size in the foreseeable future would likely not exceed 100 full-time employees, a majority of whom would be educated or hold experience at the PhD level. In the early years, staff size would be markedly smaller, perhaps as few as 10 in the first year. The program could contract for additional assistance, as needed and as practical. If adequate funding is available, the program could award or facilitate grants to outside research groups in support of program activities and goals. If only a shoestring budget is available, staff size could remain as small as 10 full-time employees for an extended period, while still making progress towards the mission. A light level of funding might translate to a staff size of about 50 full-time employees.
Assuming full funding, annual operational costs, not including grant making, are likely to remain below $30 million USD for the foreseeable future, not accounting for inflation, and would be markedly lower in early years. To help put this budget in perspective, the Florida Fish and Wildlife Research Institute (FWRI) has an annual budget of about $60 million . Sources for funding could include grants, donations, and consulting services. In time, consulting fees could generate substantial revenue, if that route is taken. Consulting revenue could be used to repay a social investment made by a major funder. Annual operational costs for shoestring and light funding levels might be about $3 and $15 million, respectively. Remaining discussions assume full funding.
The first two or three years of effort would focus on program development, including organizational and staff development and public outreach. On the technical side, initial standards would be developed for model assessment, data use, and reporting of results. As well, initial fitness (model quality) metrics would be developed, along with initial standard datasets for use in computational testing. Development would begin on local-area (small-area) models of the relationships between socioeconomic, demographic, lifestyle, public health, environmental, land use, human resource use, and other factors important to collective wellbeing. At some point in this phase, concepts and advanced designs for social choice systems would be solicited from the global public.
The next four phases, in sequence, are initial computational assessment of proposed systems, advanced computational assessment, field testing of highly promising systems, and implementation, monitoring, and networking of systems that pass field testing. Assessment, testing, and implementation would be joint efforts between principal investigators and program staff.
Given that the program represents a new frontier in applied science, the assessment, field testing, and implementation phases could occur iteratively, with each round exhibiting a greater degree of sophistication. Refinement and updating of previously implemented systems could occur on an as-needed basis.
Ongoing program efforts would include:
• Public relations, education, public outreach, and networking. As part of this, a short documentary film or similar media could be developed.
• Ongoing multidisciplinary research studies that would help shape and inform the overall program, and/or that would address specific topics, such as the development of fitness metrics.
• Testing and prototyping of data collection, data storage, user interface, analytical, and other types of instruments, systems, and tools. This could include testing via virtual (Internet) or other types of communities.
• Refinement of local-area predictive models that span a wide range of topics related to collective wellbeing (public health, crime, security, socioeconomics, demographics, lifestyle, human resource availability and use, environmental quality, etc.). These models would increasingly serve as core libraries for assessment and testing phases, and as well could assist clubs that have already implemented a system.
• Fundraising and program and staff development.
Core software systems and libraries, and potentially other core components, would be made available to the public via an open source, Creative Commons, or similar license. Especially as field trials begin, commercial opportunities would arise in developing user interfaces or other tools that sit on top of the technology stack, as well as in providing consulting, training, system maintenance, management, hardware, and more. The program itself might raise revenue through consulting services, as mentioned.
For all phases of assessment, testing, and implementation, the focus is on local, community systems organized as nonprofit civic clubs or similar. Such clubs:
• Are membership-based, nondiscriminatory, inclusive, transparent, and open to the local public.
• Do not charge a fee for joining.
• Are voluntary. Individuals can choose to join (or leave). Businesses, nonprofits, governmental agencies, and other types of organizations can also choose to join (or leave).
• Implement only limited, common-sense restrictions on who may join or remain as members, and only as required for system stability or function.
• Manage and operate their own social choice system, with help from experts, consultants, or others as desired. A club is not managed by any entity other than its members.
• Form only in jurisdictions where they are legal. Not only are they legal in large portions of the globe, they can typically be implemented without any legislative action or approval.
• Coexist and operate in parallel with existing economic, political, and legal systems.
• Operate a social choice system for its own self-governance. Its rules or decisions are not binding on nonmembers.
• Do not segregate participants from nonparticipants in their own communities.
• Are democratic by design, in that any member can choose to participate in the group problem-solving process, and is empowered to meaningfully impact that process.
• Grow organically, based on the merits and benefits of the implemented social choice system. A club as small as 1,000 individuals — potentially, a small fraction of a local population — could hold a field trial or implement a new system.
• Are designed and operate such that every community on the globe could implement a reasonably similar social choice system and obtain reasonably similar benefits. In particular, a social choice system should not exploit other communities for its own gains. Nor should a club act in a way that would be unsustainable if all clubs were to act similarly. A club would develop a clear, defined, testable path that leads to sustainability, discussed from a local and global perspective.
• Network with other clubs for stability, resilience, cooperation, trade, research, monitoring, and other types of mutual benefit and support.
After the first field trials are completed and clubs begin to implement new systems, some of the program’s attention would shift to the discovery, development, and testing of social choice systems for networks of clubs, as opposed to single clubs. While single clubs would have local impact, networks of clubs would have regional and global impact. Ideas that prove successful for single clubs could be expanded and adapted for networks, and some of the same criteria for a club would apply to a network.
The proposed club model is preferred over other approaches, in particular those that employ mandatory participation or that test and implement new systems at national or global levels. Compared to alternatives, the club model has far lower cost and risk, greater capacity for parallel testing, greater flexibility (club members would have choices regarding system design), and would encounter less political and social resistance.
The general idea is to develop social choice systems that produce such clear and pronounced benefits that they become highly popular. Participation rates for an individual club should naturally rise over time as benefits are demonstrated, and implementations should spread horizontally to new communities, in a viral fashion, such that networks of clubs expand exponentially fast across the globe.
The club model has additional benefits. In particular, it results in a flexible, adaptive global or semi-global web of overlapping networks, where each club, each network, and each network of networks is designed to excel at problem solving. Decision-making power is decentralized within each. Each can be made optimally resilient and robust, and to maximize information flow and social computation.
Similar to the original ideas for the Internet, a properly designed network of clubs can be stable in the face of stress or damage. Given that climate change, resource depletion, violence, financial instability, and other problems are already serious, and may well produce severe stress in coming decades, including stress from mass migrations, it is prudent to create a network of systems that can successfully absorb likely stresses.
In more detail, the assessment, field testing, and implementation phases for the initial (slowest) round include:
(1) Initial computational assessment. Design concepts and advanced designs for social choice systems are solicited from the global public. In addition, the program may develop its own designs or request designs from selected groups. One or several prizes could be offered. The goal of this phase is to generate an initial set of interesting or promising designs, to assess and describe those designs using relatively simple (low-resolution, abstract) computational models, and to engage as many groups in the discovery process as is feasible. This phase could last about two to three years.
(2) Advanced computational assessment. Designs that look particularly interesting or promising in initial computational studies would be tested further, using more demanding models and stricter criteria. A wide set of factors related to collective wellbeing could be assessed (economic, public health, environmental, human resource use, and so on), and fitness metrics would be applied. Testing would continue until it is clear that one or more designs are likely to be successful in field trials. This phase could last about three to four years.
(3) Field trials. Designs that pass all previous testing would undergo field trials to study system function and dynamics, verify benefits, identify weaknesses, and learn how designs might be improved. Results would be benchmarked for system comparisons. Trials could be designed to be financially safe-to-fail. For example, a bond could be issued to cover any financial injury. At the end of a trial, a club could choose to continue using the system, or terminate. Each field trial would last approximately two years, or enough time for thorough evaluation. The entire field trial phase could last about three to five years, depending on how many trials are conducted and their degree of overlap.
(4) Implementation. Once one or more designs have successfully passed field trials, and implementations are expected to be successful, the program would assist communities in the formation of clubs, the implementation of new systems, and the monitoring of systems once operational. Also, the program would shift some focus toward the development of social choice systems for networks of clubs. The implementation phase could last about two years, after which the next program round would begin. If funding allows, rounds could overlap to some degree, speeding up the process.
Including the initial program development phase, the first round of assessment and field testing, up to the start of implementation, would take about 10 to 15 years. Subsequent rounds would be considerably faster as much of the mechanics would be in place and the required skills developed. By the time the first field trials are completed, a large portion of the world’s population should have learned about the effort, and be aware of the demonstrated benefits. Networks of clubs would begin to form, and as discussed later, could be expected to expand at an exponential rate based on system benefits.
Goal of the Prize
The stated goal of the New Shape Prize is to identify one or more innovative governance models capable of addressing the most pressing threats and risks to humanity. This proposal takes a broad view of the term “governance” and suggests a metamodel as a way to engage the scientific community in the discovery, testing, and implementation of new governance models (i.e., problem-solving systems, social choice systems).
Obviously, global society would want any new model to “work well.” In fact, given the serious dysfunctions of existing systems, and current and expected social and environmental stress, the stakes are high that new models do work well. Without better models, the future may be bleak.
Therefore, it is prudent to engage the scientific, engineering, and technical communities in developing new systems. Doing so increases the likelihood that new systems will function as intended and desired, and be amenable to monitoring and assessment so that we might know when and if they veer off course.
A requirement is that models be capable of implementation within the foreseeable future. Given dysfunctions of existing systems, it is wise to explore fundamentally new designs. But it would be unreasonable to expect that new designs could be developed and implemented within a short time frame, say, several years. It might be possible to make small but meaningful improvements to current systems within such a short time frame, but probably not be possible to make the types of dramatic improvements that are needed and pointed to here.
To achieve dramatic improvements, a time frame and a definition for “foreseeable future” might be measured in decades. Indeed, if the best new designs are fundamentally different from current ones, a faster rate of change might not be advisable or possible. It takes time to implement major change. It also takes time for education and cultural and economic adaptation. Moving too fast could lead to excessive stress and risk of program failure.
The proposed program expects to generate fundamentally new and markedly better designs — and expects that these will produce dramatic reductions in risk and improvements in collective wellbeing for large portions, if not a large majority, of the global population within about 50 years. Long before that, however, systems will be operational and producing benefits in many locations around the globe, and for sizable populations.
As a rough estimation of time frame, the adult population of the world is about 5.2 billion. About 49 percent of adults now live in full or flawed democracies. Of the remaining, 18 percent live in hybrid regimes, and 33 percent in authoritarian regimes . Conceivably, a club could be allowed in any nation. Once the program begins to demonstrate pronounced benefits, there would be tremendous economic and social pressures driving new implementations. Benefits might be seen in trade, security, income, public health, and more. Nevertheless, to be somewhat conservative, assume for the moment that clubs could operate only in full or flawed democracies.
Assume that the participation rate for each club (at maturity) is 90 percent of the local adult population, as per the LEDDA simulation , and that each club has an average of 300,000 adult participants (at maturity). Maturity here means that the early fast growth rate of a club is slowing as the local participation rate approaches 90 percent. Then about 7,700 clubs could form within full and flawed democracies. Assume that in the first round of the program 10 clubs are implemented, and that each club that forms spawns or inspires up to one more during each year of its existence. For simplicity, assume there is only one program round, and that it takes 10 years before implementations begin. Then all 7,700 clubs would form within 11 years of completing the first round, or within 21 years of the start of the program.
The point is, if clubs produce pronounced benefits, and so are popular, they could spread over large portions of the globe in a reasonably short time.
The first clubs would be reaching maturity within about 20 to 30 years after inception. Thus, within 30 to 40 years of the first implementations, or 40 to 50 years from program start, about 45 percent of the world population would be members of clubs. Possibly, the percentage would be substantially higher as clubs would likely gain invitation into some areas that are now governed by hybrid and authoritarian regimes.
Keep in mind that as the size of networks expand year by year, so also do the overall benefits to global society. Long before the first clubs reach maturity, the local, regional, and global impact of networks on social and economic stability, wellbeing, and the environment would be pronounced.
One early widespread benefit could be a positive impact due to increased hope. The program could give a large portion of the world reason to believe that problems could soon be solved or successfully addressed, and that life will become better. That kind of hope could be a stabilizing factor in and of itself. Early signs of hope and its effects might be seen even as the first field trials are completing. By that time, on-the-ground experience would be gained, and a substantial body of simulation and other work would be available. The potential benefits of new social choice systems would be starting to become clear.
Limitations to the Sovereignty of Nation-States
A requirement of the contest is that governance models involve a minimum of limitations to the sovereignty of nation-states. The proposed metamodel requires no limitations to the sovereignty of nation-states. The program calls for field testing and implementation of new systems via a club model that would be legal and allowable in large portions of the globe. In most nation-states, no legislative action would be needed to initiate and operate a club. Many types of organizations (formed as nonprofits, civic clubs, businesses, or otherwise) exist in these countries and already conduct some portion of the activities discussed here. Examples include civic betterment clubs, business cooperatives, and small business, social service, and trade organizations. Buy-local campaigns exist, as do local currency programs .
Assuming that the social choice systems developed and implemented would produce pronounced benefits, and so would be popular, it is reasonable to expect that as participation rates climb, nation-states would address issues related to them. The opportunity would be for nation-states to learn from the experience of clubs, and to borrow useful ideas from them. There would be expanding social and economic pressures to do so. The proposal does not require any nation-state to alter its governance structure, however. Massive benefits could still be achieved even if governments remain static.
The Model Description section of this proposal addresses issues and concerns listed as part of the New Shape Prize assessment criteria. To highlight a few, recall that comprehensive computational assessments, field testing, and other types of testing (including testing of user interfaces) would identify problems and weaknesses of candidate systems. For example, social choice systems that did not distribute decision-making power, were easily abused, improved club wellbeing at the expense of others, were ineffective or excessively slow at problem solving, and/or lacked transparency would score low on fitness metrics as compared to systems that were more immune from these kinds of problems. Likewise, a system that would work wonderfully in an ideal world but fail in the actual world would score low.
That said, to further set the tone of the proposed program and to better describe why successful clubs and successful networks would, by definition, take the concerns of others and the planet into consideration (a topic touched upon by assessment criteria) it is helpful to step back and view societies, and their social choice systems, as complex adaptive systems.
In fact, all living systems — cells, organisms (collections of cells), and societies, which we can think of as superorganisms (collections of organisms) — can be usefully viewed as complex adaptive systems. They are complex because they consist of large numbers of interacting agents that display some degree of complicated but cohesive behavior. They are adaptive because they change in response to conditions — aside from genetic adaptation, they learn. Even single-celled organisms can learn, albeit in a rudimentary way . As already discussed, learning occurs when the problem-solving process cycles.
By viewing societies and other living systems as complex adaptive systems, and by noting that successful complex adaptive systems function as successful problem-solving systems, we can see that the ostensible purpose, call it a natural purpose, of a club, society, or other living system is to solve or successfully address the problems or challenges that matter to it — these are problems that touch upon core needs for sustenance, flourishing, and sustainability. I have called this solving problems that matter . To the degree that a living system fails to solve its important problems, it risks decay or death.
The term complexity has several technical definitions. But by the one used here, the more complex an organism (or society), the greater its capacity for problem solving. For a system of a given size, complexity is maximal when computation (in an information theory sense) is maximal [8,19]. Moreover, the more complex it becomes, the more complex its needs.
Humans have a wide variety of core needs, for which several investigators have proposed categories, or arrangements. The economist Manfred Max-Neef recognizes nine categories: subsistence, protection, affection, understanding, participation, leisure, creation, identity, and freedom . Others, including the psychologist Abraham Maslow, propose somewhat different descriptions . By extension, these are also the needs of a family, club, and society.
However we define our core needs, the long path of evolution, over countless ancestral species, has inserted them deep into our biology. Needs make our survival more likely by focusing our attention on problems that matter. If we neglect them, our problem-solving capacity suffers. Think of core needs as drivers, modulators, or input to the problem-solving process. This is why core needs appear in the center of Figure 1.
A successful social choice system helps engage participants in work and activities in which real needs are met and problems that matter are solved — in other words, meaningful efforts. Compare this ideal to current economic systems, for example. Only about 13 percent of workers worldwide are “engaged” in their jobs , one signal that meaning is sorely lacking in existing economies.
The point is, a social choice system cannot be fit if it neglects core needs.
Because we are problem-solving creatures by nature, we seek to understand ourselves and our world. Our creativity drives us forward, as does our curiosity.
But understanding ourselves and the world is an endless task. All living systems are entwined and interdependent. In a real sense, we are one big whole. Every part impacts every other part to some degree, perhaps vanishingly small or overwhelmingly large. Thus, as human civilization expands in technology, in social complexity, and in the capacity to learn, it encounters an endlessly expanding and ever more intricate web of relationships between itself and all other life. As awareness of the larger picture grows, the natural tendency (unless thwarted or subverted) is to feel ever-greater identity with the whole, and to want to explore more.
The point is that the more we learn about ourselves and the world, the more natural it becomes for us to consider the needs of others and our impacts on others, including other species and the larger ecosystem. As noted in Figure 1, concerns about others plays a central role in the problem-solving process. A hallmark of a fit social choice system is that it encourages us to make (wise) decisions that take the needs of others into account.
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By John Boik, PhD. To learn more about the R&D program, the LEDDA economic democracy framework, or to download (free) Economic Direct Democracy: A Framework to End Poverty and Maximize Well-Being (2014), visit http://www.PrincipledSocietiesProject.org.
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