Thinking is hard; arguing is harder; and arguing constructively is harder still. When I was 16, growing up in the Netherlands amid rapid social change, I struggled to have conversations that were more than noise, exchanges that genuinely helped people learn from one another. To make sense of those debates, I began informally sorting the discussions around me by the level at which people chose to engage, and over time that practice evolved into a simple but durable method I still rely on with friends, colleagues, mentors, and leaders. The essence of the method is building a hierarchy of thought, then deliberately pushing upward through it to understand the larger forces shaping any issue.
Thinking in levels is one of the simplest and most powerful mental disciplines for dealing with complex issues. Instead of reacting at a single, narrow scale, the idea is to force the mind to look at any question from at least three vantage points: personal, community, and national or systemic. This structured movement across levels does not just add detail; it fundamentally changes how problems are framed, how trade‑offs are seen, and how convincingly arguments can be communicated to different audiences.
The discipline of three‑level thinking
The core practice begins with a personal rule: never analyze an issue on only one level. At a minimum, every serious question should be examined through the lenses of the individual, the immediate community, and the broader system or nation.
– Personal level: Focus on the individual’s emotions, incentives, risks, and opportunities.
– Community level: Consider the impact on groups such as teams, clubs, workplaces, cities, or professional networks.
– National/systemic level: Examine how institutions, rules, technologies, and cultural norms shape outcomes at scale.
, This three‑level structure matters because each layer reveals different dimensions of the same reality. A decision that seems obviously right at the personal level may look problematic at the community level, and outright dangerous when extended to a national system; the reverse can also be true. By training the mind to move up and down this ladder, thinking becomes both more thorough, because fewer blind spots remain, and more elevated, because it can be translated into forms that resonate with more sophisticated audiences.
Using scales and tables to force elevation
One practical technique for elevating thinking is to introduce an explicit measure or scale for the issue at hand. The moment a scale is defined, the conversation is nudged away from isolated events toward patterns, gradations, and systemic effects.
Imagine constructing a simple table with three columns: “Personal,” “Community,” and “National/Systemic.” For any decision: changing a rule, adopting a technology, launching a policy, you deliberately fill in each column with questions and consequences. This structure functions as a kind of mental scaffold: it almost forces a small paradigm shift, because instead of asking “Is this good or bad?”, the question becomes “What does this change at each level, and how do those changes relate?”
The same applies when defining numerical or conceptual scales. For example, in the context of technology adoption, you might define a scale from 1 (purely human judgment) to 5 (fully automated decision‑making). As you move along the scale, the nature of the debate changes, from discussion of error and bias in individuals at the low end to questions of systemic bias, transparency, and governance at the high end. The scale provides a mental rail along which thought can travel to higher levels of abstraction without floating away from concrete cases.
VAR in soccer: one change, three levels
The introduction of Video Assistant Referee (VAR) in soccer provides a vivid illustration of how multi‑level thinking works. On the surface, VAR is merely a technological add‑on that allows referees to review decisions using video; in reality, its meaning shifts dramatically depending on whether it is viewed from the perspective of the individual player, the league, or the sport as a system.
Personal level: fairness and emotion
At the personal level, consider a striker accused of committing a foul in the penalty area during a crucial match. Without VAR, the referee’s on‑field decision is final; if the call is wrong, the player may feel unjustly punished, helpless, and even betrayed by the system. With VAR, that same player can “scale up” and see that another layer of review exists, offering a chance to correct an error.
From this vantage point, VAR is a psychological safety net. It can:
– Reduce the emotional trauma of decisive mistakes.
– Change player behavior by discouraging blatant dives or off‑the‑ball fouls, knowing that cameras see more than the human eye.
– Enhance a player’s trust that the outcome of a match reflects performance more than officiating error.
Here, the story is intimate and emotional. VAR is mostly about individual fairness, reputation, and the sense that one’s effort will not be undone by a single human mistake.
Community level: leagues, clubs, and fans
At the community level, the frame expands to include leagues, clubs, refereeing bodies, and fan communities. Introducing VAR now raises questions about cost, logistics, training, and the culture of the league.
Practical and cultural consequences at this level include:
– Increased resource requirements: Where a match might previously have been run by three officials, VAR often means expanding the refereeing team to five or six people, including video specialists. This raises direct costs for equipment, staffing, and training.
– New skills for officials: Referees must now master not just the rules of the game, but also the use of complex technology under time pressure and intense scrutiny. Communication with fans and players becomes more demanding, as decisions need to be explained with clarity and authority.
– Shifts in fan experience: Some fans welcome the correction of “clear and obvious errors,” while others feel that extended video checks break the rhythm of the game and dilute spontaneous celebration.
At this level, the conversation shifts from “Did the referee make a mistake?” to “Is VAR improving the overall quality and legitimacy of competition in this league?” Debates revolve around governance, investment, culture, and the shared identity of the community defined by the sport.
National and systemic level: technology and power in sport
At the national or systemic level, VAR is no longer just a tool or even a league policy—it becomes part of a broader pattern in how modern societies embed technology into institutions. When a national league or federation adopts VAR, it effectively declares that top‑level soccer now requires a technology‑intensive officiating infrastructure.
From this vantage point, several critical questions emerge:
– Standard‑setting across sports: Once VAR becomes common in major soccer leagues, it sets a new benchmark for what “serious” professional sport looks like, putting pressure on other sports and lower‑tier competitions to adopt similar systems or risk being seen as less credible.
– Technology as arbiter of truth: VAR gives technology a growing role as the supposed objective finder of truth in contested situations. Yet the system carries its own biases, determined by camera placement, frame rates, software tools, and the humans interpreting the footage.
– Inequality and access: Wealthy national associations and leagues can invest in sophisticated systems, while poorer ones cannot, potentially widening gaps in perceived fairness, commercial value, and player development pathways.
At this level, VAR is a case study in power, infrastructure, and the politics of truth. The key themes are no longer just fairness in a single match or even the integrity of a single league, but rather the kind of sports ecosystem—and by extension, the kind of society—that is being constructed.
An AI example: automated hiring systems
The same multi‑level logic applies to the adoption of artificial intelligence, for example in automated hiring systems used by large organizations. Consider an AI‑based tool that screens résumés and ranks candidates for interviews.
Personal level: the job seeker’s experience
From a personal standpoint, a job seeker submitting a résumé to a company that uses AI screening faces a new kind of uncertainty. The candidate may worry that:
– Their background does not “fit” the patterns that the algorithm favors.
– Non‑traditional experience, career breaks, or unconventional education will be misinterpreted as lack of competence.
– There is no human being to appeal to if they are rejected without explanation.
Here the core issues are emotional (anxiety, frustration, loss of agency) and practical (whether the résumé passes the automated filter). The candidate might try to “game” the system by stuffing keywords into the résumé or using AI tools to optimize wording, illustrating how technology reshapes individual strategies. The person’s sense of fairness is tied to whether they believe the AI recognizes their story accurately.
Community level: HR teams and organizational culture
At the community level, zoom out to the HR department, hiring managers, and the broader employee community. The introduction of AI in hiring raises questions such as:
– Efficiency versus judgment: HR teams may appreciate faster screening and reduced workload, but worry that over‑reliance on the tool will weaken their own professional judgment and intuition.
– Diversity and fairness within the organization: If the AI model is trained on historical hiring data that reflect past biases, it may replicate or even amplify those biases, undermining diversity goals and damaging trust among employees.
– Transparency and accountability: When candidates or current employees question rejections, HR must decide how much of the AI’s reasoning can be explained and who bears responsibility for outcomes.
In this frame, AI is not merely a technical tool; it is a community practice that shapes team composition, norms of fairness, and the internal reputation of HR as either forward‑thinking or detached and technocratic. The conversation focuses on how the system affects relationships, culture, and operational routines within a defined organizational community.
National and systemic level: labor markets and regulation
At the national or systemic level, large‑scale adoption of AI hiring tools by many organizations transforms labor markets and regulatory agendas. Key concerns include:
– Structural bias and exclusion: If many employers use similar AI systems, entire categories of workers—from older candidates to those from underrepresented backgrounds—may face systematic disadvantages, even if each company believes its process is neutral.
– Regulation and rights: Policymakers must decide whether and how to regulate AI in employment, addressing issues such as transparency, appeal mechanisms, data protection, and the right to human review of automated decisions.
– Skill and power reconfiguration: As AI tools handle more of the screening, the skills valued in HR, recruiting, and even among job seekers themselves shift. People may need to learn how to “speak algorithm” just to participate fairly in the job market.
At this level, AI in hiring is no longer a matter of one candidate and one company; it is a question about how a society allocates opportunity, how power is distributed between workers and employers, and which values—efficiency, fairness, innovation, or control—are encoded into its labor infrastructure.
Contrasting the levels: what really changes?
Looking across both the VAR and AI examples, the contrasts between levels become clear.
– At the personal level, issues are framed in terms of feelings, direct consequences, and concrete choices.
– At the community level, the focus shifts to coordination, culture, shared norms, and local governance.
– At the national/systemic level, the emphasis is on power, infrastructure, inequality, and long‑term trajectories.
Importantly, the same decision can look virtuous at one level and troubling at another. VAR feels fair to an individual player, but may be costly and divisive within a league, and could entrench technological inequality between rich and poor competitions. AI hiring might seem efficient and impartial to a single HR team, yet produce systemic exclusion when scaled across an entire economy.
This is why disciplined movement across levels is so valuable. It prevents over‑reliance on one vantage point—such as “How does this affect me?” or “What do the regulations say?”—and encourages leaders, citizens, and professionals to see the full landscape of consequences.
Matching levels to audiences and elevating dialogue
Multi‑level thinking is not only a private analytical tool; it is also a powerful way to elevate public and professional conversations. Different audiences naturally inhabit different levels of abstraction, and effective communicators learn to meet them where they are, then guide them up or down the scale.
For a general audience—fans watching soccer or candidates navigating AI hiring systems—it often works best to begin at the personal level, where the emotional stakes are clearest. Once that connection is made, the conversation can be gently lifted to the community and systemic levels by asking questions such as, “What does this mean for the way our league operates?” or “What does this do to opportunities for people like you across the whole country?”
For expert or policy audiences, the process may be reversed. Analysts and regulators often start at the systemic level, discussing standards, legal frameworks, and long‑term societal impacts. Translating these abstract debates back down into personal stories, one wrongly disallowed goal, one qualified candidate rejected by an opaque algorithm, can make arguments more vivid and morally compelling.
In both directions, the skill lies in translation: re‑stating the same core issue in language, examples, and metrics appropriate to each level, while preserving logical coherence between them. This translation reveals new “angles” where arguments become sharper and more persuasive, whether one is defending VAR, critiquing AI bias, or debating any other complex reform.
Practical routines for building the habit
Turning multi‑level thinking into a reliable habit requires deliberate practice. A few simple routines can make a significant difference:
1. Write three columns for every major decision
Whenever facing an important issue, a policy change, a technology roll‑out, a strategic pivot, draw three columns labelled “Personal,” “Community,” and “System/National.” Fill each with specific questions, risks, benefits, and examples before forming a conclusion.
2. Use scales to anchor abstraction
Introduce explicit scales: from 1 to 5 on human versus automated judgment, for example, or from local to global impact. Locate the current situation on the scale and ask what changes if the context moves one step up or down. This anchors abstract thinking in concrete reference points.
3. Deliberately reframe questions across levels
Take any question—“Is VAR good for the game?”, “Is AI hiring fair?”—and force yourself to answer it separately at each level. Then look for tensions and alignments across answers: where do personal benefits conflict with systemic harms, or vice versa?
4. Tailor explanations to one level, then expand
When communicating, start at the level your audience inhabits most naturally, then offer one step of expansion. A fan might be invited to think not just about one match, but about the league’s integrity; an executive might be brought down from national compliance debates to the lived experience of a single employee.
Over time, these small disciplines reshape instinctive thinking. Instead of defaulting to one vantage point, the mind becomes accustomed to scanning vertically, like a mental zoom lens, seeking insight in the contrasts between close‑up detail and wide‑angle context.
Thinking in levels as a leadership capability
Ultimately, thinking in levels is not only an intellectual exercise; it is a core leadership capability. Leaders in organizations, communities, and nations are constantly asked to reconcile personal stories, group dynamics, and systemic constraints. Those who can only see one level’ whether the immediate bottom line or the abstract policy framework, struggle to build trust and craft solutions that endure.
The discipline first articulated in adolescence, always push thinking up at least three levels, from personal to community to national, offers a simple but profound rule of thumb. Combined with concrete tools such as scales, tables, and structured reframing, it enables deeper analysis and more elevated conversation across domains as varied as sports officiating and AI‑driven hiring. In a world increasingly shaped by complex technologies and interconnected systems, the capacity to move smoothly between levels may be one of the most important forms of intelligence available.
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