POST-HIERARCHICAL ANALYSIS · 018
When Democratic Discourse Becomes a Struggle, Who Can Change the Rules of the Game?
A post-hierarchical analysis of Vakaris Vingilis’s interview with philosopher Paulius Gritėnas
Terms marked with ? can be explained without leaving the article.
Paulius Gritėnas warns that the norms of democratic discourse are eroding. But is it enough to call for greater decency when provocation is rewarded? A post-hierarchical response begins with a different kind of interaction: people themselves develop and choose solutions, while algorithms merely calculate the results of their evaluations.
Source: Read the original interview →
SHORT EXPLAINER · 1 MIN 21 SEC
How Omnicracy Changes Group Decisions
From competition for attention to a shared process in which people propose and evaluate alternatives, and stakeholders make the final choice.
- Domain experts produce a ranked list of solutions.
- Stakeholders select the final option.
- The algorithm only calculates participants’ evaluations.
What Does the Interview Reveal?
Gritėnas’s warning matters for more than its appeal to politicians to speak more politely. The interview links the erosion of public norms to an environment in which provocation pays: it attracts attention, increases visibility and helps mobilize voters. When incivility becomes an effective tactic, personal decency competes with the incentives built into the system. The question is not just how people speak, but how they can sustain a shared basis for action.
Context: When a System Protects Its Own Weaknesses
The openness of democracy creates a paradox: it must protect dissent and criticism, yet those same freedoms can be used to undermine public trust. This does not mean that everything is permitted in a democracy. The problem runs deeper: even lawful communication can sustain destructive incentives, while attempting to eliminate them through prohibitions alone can compromise the freedoms being defended.
From the perspective of this analysis, the helps reproduce a hierarchical relationship: some compete for the right to make decisions, while others remain their audience. Scandal becomes valuable not because it solves a problem, but because it generates visibility. A system can condemn this behaviour while simultaneously rewarding it. We should therefore ask not only how to “cure” it, but how people might organize joint action beyond the struggle for attention.
What Makes Gritėnas’s Diagnosis Valuable?
Public Power Entails Greater Responsibility
The interview reminds us that standards appropriate to private conversation are not necessarily adequate in public life, especially for those holding positions of responsibility.
Democracy Also Depends on Unwritten Norms
The erosion of respect and trust is recognized as a problem for political life as a whole, rather than simply a matter of poor taste.
Ethics Cannot Be Separated from Incentives
The media’s responsibility is related to its economic and technological environment, not merely to the will of editors.
Defending Something Must Not Destroy It
Abandoning democratic principles cannot automatically be regarded as an appropriate way to protect democracy.
The Post-Hierarchical Divide
Defend the norms of democratic discourse by strengthening leaders’ responsibility, encouraging media restraint and limiting the destructive effects of digital platforms.
Rather than electing people to decide for others, enable people themselves to develop alternatives and choose the final decision. The decidement protocol establishes incentives, people evaluate ideas, and algorithms only calculate the results of those evaluations.
The changes not merely the style of political communication but the organization of decision-making authority itself. Representative democracy establishes governing power through elections. does not require representatives to be elected: people themselves develop and choose solutions. This is not another consultation channel after which a leader makes the decision on their behalf.
The interview serves here as a point of departure. What follows is an independent proposal developed in this culture.lt analysis, not a model attributed to the interviewee or an assumption that he endorses it.
The essential change is not a more polite competition for power, but a different architecture of human interaction and shared decisions.
Not Debates, but the Search for New Solutions
is not the sum of opinions gathered separately. In a single interactive group, people propose solutions to a defined task and evaluate proposals submitted by others. A collective-intelligence project does not involve debates: there is no opponent to defeat or faction to defend. Someone else’s idea can help a participant discover a better alternative.
The rule “evaluate others’ proposals before submitting your own” is primarily intended to prevent duplication. Having examined existing solutions, a participant can offer something new rather than repeat what has already been proposed. A repeated proposal receives a score of zero—a penalty for duplication, not for expressing an unpopular view. Each submission should contain one clearly defined solution; creativity is directed towards finding better alternatives within the scope of the task.
How Could Collective Decidement Work?
Omnicracy rests on two successive stages of decidement. In the first, people knowledgeable about the issue seek the best alternatives in a shared collective-intelligence environment. In the second, the people affected by the decision evaluate those alternatives and make the final choice. There is no need to elect representatives who will later decide for everyone: expertise helps uncover possibilities, while the participation of stakeholders determines the common course of action.
For the problem raised in Gritėnas’s interview, the task could be formulated as follows:
What rules for informing a community and making shared decisions would reward valuable proposals rather than provocations?
Stage I. People with Relevant Expertise Develop Solutions
Those knowledgeable about the issue choose to join the task on the basis of their knowledge and experience. In a single anonymous collective-intelligence environment, they evaluate existing proposals and develop new ones. Before submitting an idea, they review what has already been offered so as not to repeat it. Each idea is rated from −5 to −1 when a participant disagrees, and from +1 to +5 when they agree. Zero is not a neutral rating: it is a penalty for repetition, triviality, straying from the task, combining several separate ideas in one submission, or revealing the author’s identity. This encourages clear, independent solutions that add value to the task.
The outcome is a ranked list of alternatives, showing the most strongly supported solutions and, where present, ideas that attract conflicting assessments and reveal polarization. Experts expand the range of choices; they do not choose on everyone else’s behalf.
Stage II. Stakeholders Make the Final Choice
In the same collective-intelligence environment, stakeholders evaluate the alternatives identified by those with relevant expertise and choose the final solution. At this stage, social competences are especially important: the ability to appreciate consequences for the community, reconcile different interests and consider the common good. This is neither a conventional survey nor an election of representatives: the stakeholders themselves make the decision.
Who Is Entitled to Participate?
Neither delegation nor a selection committee is required. The necessary competences are defined by the task, and individuals decide for themselves whether to participate. A short AI-generated knowledge test and the accumulated through earlier participation help establish readiness. Those without sufficient weight can begin as observers, evaluating proposals until their assessments correspond closely enough to those of experienced participants for their contributions to count and for them to submit ideas of their own. Participation thus remains accessible without equating the quality of decisions with a merely formal entitlement to express an opinion.
Who Evaluates: People or Algorithms?
People evaluate solutions. Participants remain anonymous to one another, but their proposals and evaluations are accounted for. The sets the shared rules of interaction and reward, while the algorithm mechanically calculates evaluation results, individual scores and the effects of competence weights. It neither determines what is right nor examines participants’ motives or behavioural patterns. The algorithm serves collective decidement; it does not replace it.
How Can Personal Benefit Become an Incentive to Serve the Common Good?
Here lies a central difference between collective decidement and the attention economy. In the classic , seeking an individual advantage can leave everyone worse off. Collective decidement aims to reverse those incentives: higher scores go to participants whose evaluations are close to the collective assessment that emerges through honest participation. Personal interest is thereby connected to the effort to judge impartially with regard to the common good. This can be understood as an .
The result is a seemingly paradoxical incentive: a participant may seek the highest possible personal score, yet doing so makes impartial evaluation advantageous. Because they do not know in advance how others will rate an idea, they try to assess it as, in their understanding, an honest and socially responsible person would. Defending a narrow group interest may therefore lower their own score. The measure becomes not anyone’s status, but the value of a solution to the community, including its regard for vulnerable members of society and even for nature, which cannot defend itself.
Collective Wisdom Is Not Prescribed: It Emerges
is not a prewritten set of correct answers. Each participant, by trying to evaluate impartially, contributes to a collective result that none can know in advance. In this sense wisdom emerges from many independent evaluations oriented towards a shared principle. The algorithm merely calculates their outcomes; it is not the source of wisdom or moral norms.
An analogy with Kant’s categorical imperative helps explain that orientation: evaluate as though the principle guiding your judgment could apply universally. Participants do not know how others will rate the same idea, so they cannot simply conform to a majority. Anonymity reduces the influence of authority, group loyalty and public pressure, while the accounting of evaluations encourages an independent search for a socially grounded decision. The same people, interacting anonymously, may therefore produce a very different collective assessment from the one they would express in public—even to their own surprise. The resulting agreement is resonance: the convergence of independent evaluations, not the reproduction of an already known majority opinion. As the influence of narrow group interests diminishes, intersubjectivity can give rise to objectivity. Collective intelligence is not a either: there is no future event whose outcome must be guessed. The shared assessment comes into being through the process of developing and choosing solutions itself.
AUDIO ESSAY · 21 MIN 14 SEC
How Omnicracy Replaces Political Debate
An audio exploration of moving from adversarial debate to the collective search for and evaluation of solutions.
Choosing a Solution Is Not the End
Collective decidement can also be used to agree in advance on who will implement a chosen solution, how funds will be allocated and how progress will be monitored. Implementers retain responsibility, but this does not give them the authority to unilaterally change the agreed course. When circumstances change, a new task can be addressed through the same two-stage process.
VIDEO ESSAY · 10 MIN 43 SEC
Changing the Rules: From Political Conflict to Collective Decidement
A visual explanation of the architecture through which participants develop, select, and revisit shared decisions.
Change Can Begin Within Communities
Applying omnicracy need not wait for an entire state system to be redesigned. Companies, local communities and networked communities can begin with their own practical questions: develop solutions according to relevant expertise and choose them together, without transferring decision-making authority to elected representatives. Examples of such tasks and the supporting infrastructure are presented in the METACON project catalogue and on omnicracy.net.
The best solution is not necessarily a permanent one. The protocol creates incentives for creativity, honesty and responsible judgment, but circumstances change. Collective decidement therefore seeks the best solution for the conditions at hand. Anyone may propose reopening a question; if a sufficiently large group supports doing so, the process begins anew without waiting for a predetermined date. In this way a community can learn from consequences and adapt while retaining its capacity to act.
From the Struggle for Attention to Shared Decidement
The crisis of democratic discourse discussed by Gritėnas raises questions not only about ethics in public life but about the incentives shaping behaviour. As long as political rewards depend on attracting attention, even well-founded calls for greater responsibility may be weaker than the logic of competition.
Omnicracy offers another path: people with relevant expertise jointly develop alternatives, stakeholders choose a solution, and every participant is encouraged to evaluate in a way that connects personal success with the common good. The protocol organizes interaction, people create the decision, and the algorithm calculates their evaluations.
Overcoming the crisis of democratic discourse may begin not with new demands on politicians, but with giving people the means to make decisions together.
People Decide; Algorithms Only Calculate
flowchart TD
A["Attention economy"] --> B["Provocation is rewarded;<br/>norms of discourse erode"]
B --> C["Response proposed in the interview:<br/>responsibility, ethics, regulation"]
B -.->|"Post-hierarchical divide"| D["OMNICRACY<br/>No representatives need be elected"]
D --> P["PROTOCOL<br/>Personal scores are linked<br/>to socially responsible evaluation"]
subgraph KI["THE SAME COLLECTIVE-INTELLIGENCE ENVIRONMENT"]
direction TB
I["STAGE I · EXPERTISE<br/>Develop the best solutions"] --> L["Ranked alternatives<br/>and visible polarization"]
L --> II["STAGE II · STAKEHOLDERS<br/>Choose the final solution"]
M["THE SAME ALGORITHMIC PRINCIPLE<br/>Only calculates evaluations, scores<br/>and specialization weights"]
I -.->|"Human evaluations"| M
II -.->|"Human evaluations"| M
end
P --> I
P --> II
II --> F["Implementation and oversight<br/>of the chosen solution"]
F -.->|"New questions of selection, funding or adjustment"| I
classDef closed fill:#f3f1ec,stroke:#9b978f,color:#25261f
classDef post fill:#fff8f4,stroke:#a93b2b,color:#25261f,stroke-width:2px
classDef shared fill:#e8eee4,stroke:#73866a,color:#25261f,stroke-width:2px
class A,B,C,F closed
class D,I,L,II post
class P,M shared