POST-HIERARCHICAL ANALYSIS · 010

Not How Many of Us, but Who Will Decide How We Live

An analysis of Erika Vaiginienė’s article “Klausimas – ne kiek mūsų bus 2050-aisiais, o kaip gyvensime” (“The Question Is Not How Many of Us There Will Be in 2050, but How We Will Live”)

Terms marked with ? are explained without leaving the article.

Erika Vaiginienė liberates the demographic future from a single fateful number. Yet multiple scenarios do not in themselves create a shared future. The real break occurs when the people whose lives those scenarios describe become their authors, evaluators and continuous revisers.

From demographic anxiety to quality of life

The article by Dr Erika Vaiginienė—a researcher at Vilnius University’s Faculty of Economics and Business Administration and an expert in strategic management and foresight—opens with a figure that could easily become a formula for collective anxiety in Lithuania: by 2050, this small EU country may have about 2.2 million inhabitants, roughly one-third of them over 65. Vaiginienė immediately rejects a fatalistic reading of that number. A smaller state is not necessarily a weaker state; the decisive question is whether its institutions, services, regions and communities will be redesigned in time.

In this sense, the article is less a demographic forecast than a manifesto for strategic thinking. Demography is treated as a complex system shaped by economics, geopolitics, climate change, epidemiological threats, migration, technology, language, regional infrastructure and people’s confidence in the future. Vaiginienė argues that Lithuania should not try to guess a single exact figure for 2050, but should consider different scenarios and ask what decisions need to be made now.

Context: when the future no longer fits inside one ministry

Lithuanian demographic policy has long been discussed in separate compartments: fertility, emigration, immigration, regional depopulation, ageing and the financing of social services. This division is administratively convenient because each institution receives its own indicator and policy package. In lived reality, however, these factors do not operate separately. A young family will not choose a region solely because housing is affordable if there is no school, doctor, work, transport or cultural life. Businesses do not relocate to places without workers, while workers do not settle where services are disappearing.

Vaiginienė accurately identifies institutional “silos”: one policy encourages people and businesses to settle outside the major cities, while another—guided by narrowly calculated efficiency—closes regional schools and healthcare facilities. This is more than a failure of coordination. It is the outcome of a fragmented decision architecture: each hierarchy optimises its own part, although society lives in the whole.

The concept of “smart shrinkage” is important here. It rejects both the illusion that population decline can be quickly “fixed” with a single payment and passive resignation to decline. The state must adapt to a smaller and older society while preserving the possibility of later renewal.

What the article does especially well

Where Vaiginienė’s argument ends and post-hierarchical analysis begins

The post-hierarchical break: the future cannot remain an expert document

Vaiginienė moves from one forecast to several possible scenarios. Post-hierarchical analysis goes further: with collective intelligence, society itself creates, evaluates and continuously refines possible courses of action.

Several scenarios alone do not remove hierarchy. They may still be produced by a closed group of experts, while an institutional centre alone chooses which path is appropriate. One central forecast is then replaced by several centrally produced alternatives, but decision-making power is not redistributed.

In post-hierarchical omnocracy, a decision is formed in two stages. In Stage I, competent experts create, test and evaluate alternative scenarios and courses of action in a collective-intelligence environment. In Stage II, interested and affected people use the same environment to evaluate the expert alternatives and make the final decision. Expertise remains indispensable, but the power of final choice no longer belongs to an institutional centre.

The subject of demographic policy would become an interacting society: families, young people, older people, regional residents, diaspora members, migrants, teachers, medical professionals, municipalities, businesses, cultural actors and experts from many fields. Knowledge of the system is distributed. An expert can model age structure; a resident knows why a family actually left a town; a municipality sees service costs; and an unpaid carer knows the hidden price borne by a household. Collective intelligence turns these different forms of knowledge into one interacting decision system.

Not a fixed target, but a continuously tested direction of values

Here the post-hierarchical approach departs most sharply from conventional strategic planning. It does not fix one date as the ultimate horizon or freeze one goal that must merely be implemented. The planning horizon moves with changing reality: visible problems are addressed, consequences are observed, alternatives are updated, and the next step is chosen according to how well it preserves the chosen direction.

That direction comes from universal values, not from a single future state proclaimed in advance. Within a collective-intelligence environment, these values become practical criteria for evaluating proposals. A participant tries to judge an idea not by how useful it is to a narrow group, but by how it might be judged by a wise collective seeking the common good.

Collective wisdom is not known beforehand and cannot be declared by a centre. It emerges from independent participant evaluations. If participants receive the highest recognition when their judgements align with the emerging collective assessment, it becomes rational to use universal values rather than push a narrow group interest. The incentive structure of the technology itself thus directs participants towards a more general solution.

Institutional silos are an architectural problem, not merely a communication problem

The usual answer to institutional incoherence is more coordination: an interdepartmental working group, a strategy, a coordinator or a new committee. This may help, but it usually places another hierarchy above the existing ones. Information travels upwards, is compressed into reports and later sent down as decisions. Complexity is addressed by increasing the burden on the centre.

The post-hierarchical response is different. It requires a shared collective-intelligence field in which institutions, experts and affected groups see not merely one another’s reports but interconnected alternatives and their consequences. An education decision could not be evaluated only by cost per pupil; it would be connected to access to healthcare, jobs, transport, housing, community resilience and the region’s long-term capacity for renewal.

This is not a universal plebiscite on every technical issue. First, knowledgeable participants prepare and evaluate well-grounded alternatives in a collective-intelligence environment. Then the people affected by the decision, also acting through collective intelligence, make the final choice among them. Their evaluations also reveal polarisation—issues on which participants diverge and which need further investigation.

Fertility policy: from managing people to creating trust

Vaiginienė rightly stresses that fertility cannot be explained by income alone or changed by a single financial incentive. The decision to have children is connected to confidence that the future will be worth passing on to another generation. This is not only a financial category but an existential and institutional one.

Hierarchical policy often treats the family as a producer of a desired indicator: the state sets a target, selects incentives and expects behavioural change. A post-hierarchical approach would begin not with “How do we encourage people to have more children?” but with “What conditions would allow different people freely to want and be able to form a family?” Answers would not be gathered through a survey limited to prepared options, but through an interactive system for proposing and mutually evaluating ideas. Such a process could identify factors invisible to the centre: unpredictable working hours, housing insecurity, loneliness, weak support networks, distrust of institutions or incompatible service rules.

Community must not become a cheaper substitute for the state

The most problematic part of the article is the claim that some state services should in future “return to families and communities”. This may signify healthy subsidiarity: decisions and support move closer to people, relationships become stronger and local people gain more autonomy. But it may mean something quite different—the transfer of fiscal burdens and care work to families, most often to women, without transferring resources or decision-making power.

A community is not strong when the state abandons its responsibilities to it. It is strong when responsibility arrives together with resources, data and the right to decide.

In a post-hierarchical system, the family or community is not the lowest tier of service delivery. It is a decision-making subject. Local communities make final decisions on local matters through collective intelligence; networked communities do so on matters concerning their networks. Before a function is transferred, participants collectively assess whether the community wants it, what expertise and funding it requires, how vulnerable people will be protected and how unpaid labour will be measured. Government no longer distributes final decisions from above; it implements decisions made by society and provides the infrastructure they require.

Regions: not identical services, but an equal right to a viable life

It is impossible to maintain an identical school, hospital or transport system in every locality. Yet economic efficiency cannot be the sole criterion. Closing services changes the future of a place: the decision does not merely respond to decline but accelerates it. Feedback loops must therefore be evaluated.

A post-hierarchical regional policy would allow different territories to create different combinations: multifunctional schools and community centres, mobile healthcare teams, infrastructure shared by several municipalities, hybrids of remote and physical services, and links between local business and vocational education. Local communities would make final decisions affecting their territory through collective intelligence, while society at national level would decide common questions of redistribution, rights and data interoperability. Successful local models could spread not as compulsory instructions but as tested alternatives that other communities may choose.

Migration and Lithuanian identity: integration as a reciprocal process

Vaiginienė’s open conception of Lithuanian identity is an important counterweight to demographic defensiveness that imagines the nation as a shrinking, closed body. Nevertheless, the word “assimilation” can retain a one-way logic: newcomers must merge into an already defined centre. In a post-hierarchical society, integration would be the reciprocal creation of culture. The Lithuanian language and constitutional values remain a shared field, but new members are not merely objects to be absorbed; they also expand the country’s shared experience, capabilities and imagination of the future.

Integration policy should therefore be evaluated by long-established residents and newcomers, as well as schools, employers, cultural institutions and municipalities. Only then can symbolically attractive measures be distinguished from those that genuinely create linguistic participation, mutual trust and a sense of belonging.

AI can reduce linguistic and geographic distance, but it cannot decide what kind of Lithuania we want

The article perceptively identifies a dual technological movement: artificial intelligence reduces linguistic and geographic distance, yet may simultaneously increase the need for consciously chosen physical connection. From a post-hierarchical perspective, AI matters not primarily as a machine for automating services, but as supporting infrastructure capable of processing many proposals, modelling scenarios and helping people understand the consequences of alternatives.

AI must not, however, become a new centre of demographic policy. A model can calculate where it is cheapest to operate a school, but it cannot determine the value of a community’s capacity to continue. It can forecast care needs, but it cannot decide what burden of unpaid family care is just. Values, priorities and acceptable risk must emerge through human interaction. AI can augment collective intelligence, but it cannot politically replace it.

How post-hierarchical demographic decision-making could work in practice

1. A shared problem field. Demography is considered not along ministerial boundaries but as an interconnected system of life: family, work, housing, health, education, migration, culture, regions and technology.
2. Multiple scenarios without a fixed horizon. In a collective-intelligence environment, experts prepare several testable versions of development and action without tying strategy to a single terminal date or immutable goal. The horizon moves; scenarios, assumptions and subsequent steps are updated in light of new data and actual results.
3. Accountably anonymous proposal and evaluation. Participants submit and assess proposals without seeing an author’s status or adapting to institutional authority. Solutions are separated from office, fame and political affiliation.
4. Interaction among different forms of knowledge. Experts contribute models, data and method-based alternatives; people contribute knowledge of their circumstances, locality and lived experience of services. Everyone evaluates others’ proposals in the same environment, allowing expert and experiential knowledge to complement one another without treating them as identical.
5. Visibility of polarisation. The aggregate result is not the only output. The system identifies questions and alternatives on which participants diverge. Polarisation is a signal for further analysis and a renewed search for solutions.
6. Local experiments. Instead of imposing one reform on all of Lithuania, different models of services and community care are tested in several regions.
7. Universal values and continuous feedback. Decisions are treated as testable hypotheses, not final commands. Participants evaluate them through human dignity, freedom, justice, mutual responsibility and long-term viability; results return to the common platform and shape the next actions.
8. Society makes the final decision. Local communities decide local matters, relevant networked communities decide network-level matters, and society at national level decides country-wide questions—all within a collective-intelligence environment. Institutions implement these decisions rather than replacing them with decisions from above.

From smart shrinkage to smart collective action

“Smart shrinkage” is a valuable strategic concept, but it remains administrative until we know who defines what counts as smart. To the centre, closing a small school may appear smart. To a family, preserving a place where a child is safe and a community still has a future may be smart. For the state, reducing unit costs may be smart; for a region, retaining a critical mass of people and capabilities may matter more. None of these perspectives is automatically correct. Their relationship must be resolved, not proclaimed.

Post-hierarchy does not abolish strategy, institutions or experts. It changes their roles. Strategy becomes a shared learning system rather than a closed document. Institutions no longer defend their own silos of competence; they maintain a common decision infrastructure. Experts do not declare the only rational future; they help society see alternatives, consequences and uncertainty.

flowchart TD
        A["Current population"] --> B["Different scenarios for 2050"]
        B --> C{"Who creates, evaluates and decides?"}
        C -->|"Institutional centre"| D["Vaiginienė: scenario analysis"]
        C -->|"Post-hierarchical process"| E["Stage I: expert alternatives in a CI environment"]
        E --> F["Universal values"]
        F --> G["Stage II: society decides in a CI environment"]
        G --> H["Feedback and the next step"]
        classDef conventional fill:#f3f1ec,stroke:#9b978f,color:#25261f
        classDef post fill:#fff8f4,stroke:#a93b2b,color:#25261f,stroke-width:2px
        class A,B,C,D conventional
        class E,F,G,H post