Weak EMH: Water flows downhill.
Strong EMH: Rivers can't exist.
LMH: Markets flow like sand, not like water. There's an angle of repose.
Something I feel like is missing from this post is what does LMH predict about markets, or what is the evidence of LMH? Like what is an example of an empirical observation about the stock market that contradicts EMH, but is consistent with LMH? The "Consequence" section is very abstract, I'd be interested in hearing something more concrete. For example, can LMH explain the result from De Bondt & Thaler (1985), "Does the Stock Market Overreact?"
On the contradiction point: LMH isn't looking for a contradiction from EMH. More so, it's claiming that when you model friction, cognitive cost, etc. realistic market parameters correctly, the most efficient markets that emerge from the real world will still be, at best, lazy.
The abstract research thesis here is that LMH theory should give us information about which directions to extend EMH-based economical models towards, to make them more accurate about real world markets.
(Meta: I considered giving more examples in the original post, but I felt like the terms I use are very easy to overload. I aim to write a post that is primarily about examples in the future - something like "Lazy strategies" that talks about instances of lazy decisionmaking in the real world, and it's consequences.)
Skimming the paper:
> While the overreaction hypothesis has considerable a priori appeal, the obvious question to ask is: How does the anomaly survive the process of arbitrage? There is really a more general question here. What are the equilibria conditions for markets in which some agents are not rational in the sense that they fail to revise their expectations according to Bayes' rule? Russell and Thaler 24 address this issue. They conclude that the existence of some rational agents is not sufficient to guarantee a rational expectations equilibrium in an economy with some of what they call quasi-rational agents. (The related question of market equilibria with agents having heterogeneous expectations is investigated by Jarrow 13.) While we are highly sensitive to these issues, we do not have the space to address them here. Instead, we will concentrate on an empirical test of the overreaction hypothesis.
The paper explicitly sets aside the question of why this inefficiency persists. LMH is an attempt to explain why this inefficiency makes sense from the perspective of individual economic agents, why the behavior that generates it is generally adaptive for the agent, even when it loses them money in markets specifically.
Exploring:
Claude pointed me into the direction of McLean & Pontiff: Does Academic Research Destroy Stock Return Predictability? (2015). Then I came across McLean, Pontiff, Reilly: Taking sides on return predictability (2025), which states:
> We assess how nine different categories of market participants trade relative to a comprehensive forecasted-return variable based on 193 predictors. Firms and short sellers tend to be the smart money—both sell stocks with low-forecasted returns, and their trades predict returns in the intended direction. Retail investors trade against forecasted returns. Retail investors’ and institutions’ trades predict returns opposite to the intended direction. This poor trading performance is driven by trades in stocks with either high- or low-forecasted returns. The forecasted-return variable predicts returns more strongly in stocks with more intense retail trading, consistent with retail investors exacerbating mispricing.
Of course, cherry-picking is easy. But this is the kind of result that seems consistent with LMH - active retail investors hold concentrated positions (which implies attention and amplifies reactivity), and are focused on more publicly available information than non-retail investors (since they don't have the kind of investment in their own market modelling that non-retail money has), so they focus on local opportunities. The retail investors who do the most market actions are eager. Eager-local loses to eager-global (institutional investing, who have better, but more expensive models), and to lazy-local (investing in index funds / fire and forget investing).
Just to make it concrete what LMH contributes, besides terminology: I think behavioural economics would predict that "retail investors are more impulsive than institutional investors, therefore they will overreact, having worse returns".
The LMH addition here are these claims:
- "It is adaptive for an economic agent to pay more attention and be more reactive in places where a lot of their portfolio has been invested." (This is clearly rational regarding housing or employment!)
- "It is adaptive for an agent to act more frequently in environments with fast feedback loops." (And in environments that are adversarial over this, such as gambling or markets where HFT and better models than yours exist, this is a losing strategy.)
The pattern in both cases: the eager-local strategy is generally adaptive, and markets are one of the specific domains where it isn't. Behavioural economics documents this category of failures. LMH aims to explain why the failing strategy was originally selected for.
Hypothesis: Real markets are not irrationally lazy. They are lazily rational.
The Efficient-Market Hypothesis
EMH assumes costless information, costless cognition, and costless execution. It models a theoretically optimal market.
The friction of real markets is well understood. The Lazy-Market Hypothesis is for modelling markets with friction.
The Lazy-Market Hypothesis (LMH)
Once you price labor, capital, risk, and cognition as real costs, the Efficient-Market optimum stops being the target. Chasing the EMH optimum would cost more than the expected return.
Claim: the rational agent is the lazy agent. Laziness isn't a deviation from rationality under friction; laziness is what rationality looks like once friction is in the model. The descriptive observation "real markets don't reach EMH efficiency" is also a description of real agents at their actual optimum - just not the EMH optimum.
The satisficing rule: stop spending effort when marginal benefit of effort equals marginal cost.
This is descriptive, not normative: it's a claim about which agents survive selection in friction-priced environments, not a claim about ideal rationality. Agents who miscalibrate their laziness function - in either direction - get outcompeted.[1]
Why rational agents are lazy
Grossman and Stiglitz (1980) showed that a perfectly efficient market can't fund its own price-discovery substrate: equilibrium has to sit short of the limit. LMH generalizes the move from information to effort.[2]
The laziness 2x2
Rational agents satisfice their exploration budget against expected environmental volatility, and the four cells of the 2×2 describe categories of agent behaviour: under-spending (lazy-global), over-spending in non-volatile environments[3](eager-global), spending well but committing too hard (eager-local), and the historical winner[4]: spending modestly and committing modestly (lazy-local).
Lazy markets
In lazy markets, equilibrium rests on agent laziness. Both sides form an effort frontier: each invests more only when they think the adversary does. Credit-card fraud, low-volume markets, and cybersecurity all sit in lazy equilibria. Rational agents in adversarial fields satisfice their defence against the expected capability of their adversary instead of maximizing it, and the attacker satisfices symmetrically, aiming to just barely beat the current defence rather than maximize offensive capability. Agents who try to maximize either side end up overspending in a region of diminishing returns and get outcompeted by lazier rivals.
When the effort frontier shifts
Once upon a time, a castle was the gold standard for area defence. Besieging was slow and costly and castles were otherwise nigh impenetrable. After explosives and artillery came in, some agents reacted with better walls, and it didn't work. Castles became a liability.
This describes an effort frontier shock: a change in the cost or capability landscape that invalidates the prior satisficing equilibrium. Drawing from the LMH, we can model these shocks.
Effort frontier shocks create opportunities to create value by adaptation. Who benefits from this?
Adaptation requires slack. Unspent effort budget converts directly into adaptation capacity. The eager agents already committed their efforts. (Other out-of-scope adaptation qualities[5])
Hypothesis: Lazy-local agents, by definition, hold more slack than eager agents. This makes them advantaged in adapting to opportunities after frontier shock, but they pay a cost for suboptimality under the old conditions the eager agents committed to.
Consequence: Effort frontier shocks:
Elaboration: When the frontier moves, agents with high-commitment, low-monitoring investments get hit hardest - they're slower to detect the shift and slower to redirect once they do. Sunk-cost dynamics make this worse: the first instinct on noticing a shock is usually to double down on the existing commitment rather than abandon it. Castles got thicker walls before they got abandoned.
Closing
When real agents who are winning and surviving look irrational, it often means we're not modelling their targets and cost functions in full. The interesting move isn't to scold the agents. It's to ask what the frontier looks like, and what would have to change for their satisficing point to land somewhere else.
When agents are miscalibrated, it's often easier to move the environment than to move the agents.[6]
Real markets are not irrationally lazy. They are lazily rational.
Appendix
Open questions[7].
Related literature[8].
Over long enough time horizons. ↩︎
See "Generalizing Grossman-Stiglitz from information cost to effort cost." in Open Questions. ↩︎
Why not choose your strategy based on the expected volatility? Volatility detection is itself effortful. The calibration of optimal laziness is itself subject to LMH. ↩︎
In complex, volatile environments, generalists with slack tend to win. Hyper-specialists (eager-local) dominate their narrow slot while conditions hold. Pure explorers (eager-global) rarely accumulate enough fitness to persist. The animals you've heard of are mostly lazy-locals: good-enough at local-enough tasks to exploit and survive, but carrying enough reserve capacity to ride out shocks rather than be caught rigid by them. (In the longer run they become generalists, because each shock that doesn't kill them incentivizes a new strategy.) (What about lazy-global?[9]) ↩︎
Some adaptation qualities:
↩︎See Astral Codex Ten: Society is fixed, Biology is Mutable for a cross-domain parallel ↩︎
Open questions:
- Current trends in effort frontiers:
- What the LLM era does to
- effort-floor institutions? (Zoning complaints, grant applications, text applications in general)
- attacker-defender dynamics (Cybersecurity, physical security)
- Other live frontier shifts: (Remote work? Renewable energy? Cryptocurrency?)
- "Lazy economics":
- Generalizing Grossman-Stiglitz from information cost to effort cost.
- What survives, what breaks, and what new equilibria appear when the substrate being priced is the full stack (search, evaluation, execution, monitoring) rather than information acquisition alone? Sims' rational inattention covers part of this for attention specifically; the full generalization seems open.
- Who pays for exploration?
- Could generate interesting predictions on ecosystem composition
- Implications for foundational research funding
- How legibility of output correlates with local eagerness instead of global value
- Do you actually get better foundational research if you measure proxies for the output value (citations, status, optimizing funding applications), or should you just give researchers budgets and let them do whatever?
- Rational risk tolerance
- Implications of real agent's calibrated laziness functions:
- Correlated laziness and systemic fragility: when many agents satisfice against the same threat model, the resulting monoculture is locally stable and globally fragile (2008 risk models, monocultures, antibiotic regimes). LMH may have something to say about market-level fragility that EMH-plus-friction doesn't. Future work.
- Performance of eager-global agents under effort frontier shifts and value frontier shifts
- Does the hypothesis hold that eager-global strategies are stabler over effort frontier shift but unstable over value frontier shifts?
- Do some agents deliberately aim at deeper values to outcompete agents targeting shallower ones - trading short-term efficiency for shock-resistance across value-frontier shifts?
- See "Layers of value globality"
- Laziness-function updates:
- How to affect agent laziness?
- When do agents shift their positions in the 2x2 (possibly within their quadrant, but still relevantly)? Can a lazy-local agent 'ascend' to a global-lazy agent if they happen to get lucky and find a good global gold vein to extract from? Can an eager agent weather a shock and realize they need more slack?
- Also see "Is lazy-global a selectable strategy?"
- Lazy philosophy:
- How do agents time discounting (affects local vs global) and laziness functions (eager vs lazy) relate to each other?
- Modelling the effects of effort frontier shifts (cost/capability landscape changes) versus value frontier shifts (what counts as valuable changes):
- Layers of value globality.
- Shallow-global values: Values that may shift based on megatrends or regulation.
- Medium-global values: Objectives that have stayed constant over capitalism
- Deep-global values: existential safety
- Can agents aim for deeper values to outcompete agents with shallower objectives?
- Implications for values of agents:
- LMH applications to value drift (of individuals, of organizations)
- LMH applications to addressing existential risk being irrational from the perspective of most real economic agents.
- agent-rationality question: With rationally lazy modelling, LMH-rational agents won't fund x-risk because the individual cost-benefit math fails.
- Implications for what kinds of coordination or externalization could change this.
- model-coverage question: x-risk is a deep-global value, and the layers-of-globality extension is what's needed to make the framework able to talk about it at all.
- Where are the lazy-global agents?
- Is lazy-global a selectable strategy?
- Hypothesis: You cannot aim at lazy-global, but we can recognise lazy-global? Can we only recognise lazy-global in retrospect?
- Does evolution-at-scale effectively produce lazy-global outcomes even though no individual round of selection aims at them? If selection is locally lazy-local but the surviving distribution is globally weighted toward lazy-global, what does that tell us about which other systems (markets? cultures? research ecosystems?) might have the same structure?
↩︎Related literature:
- Psychology:
- Simon (1956) Rational Choice and the Structure of the Environment: satisficing / bounded rationality. LMH treats satisficing points as equilibrium properties of markets rather than a cognitive property of agents, aims to model these equilibria.
- Economics:
- 1980 Grossman-Stiglitz: https://en.wikipedia.org/wiki/Grossman%E2%80%93Stiglitz_paradox,
- perfectly informationally efficient markets are an impossibility since, if prices perfectly reflected available information, there is no profit to gathering information, in which case there would be little reason to trade and markets would eventually collapse
- 2003, Sims: Implications of Rational Inattention https://www.sciencedirect.com/science/article/abs/pii/S0304393203000291
- 2025: Stanisław M. S. Halkiewicz: The Omniscient yet Lazy Investor https://arxiv.org/pdf/2510.24467
- (A lot more economics I've never read.)
- Internet:
- Yudkowsky, Eliezer: Inadequate Equilibria: https://equilibriabook.com/
- MacKenzie, Patrick: The optimal amount of fraud is non-zero https://www.bitsaboutmoney.com/archive/optimal-amount-of-fraud/
↩︎Almost no successful agent starts at lazy-global. They start lazy-local (or eager-local), and then luck out. Cyanobacteria didn't select for their waste product to restructure the planet's atmosphere. They were just metabolizing. The "global" part is a retroactive reclassification by an observer who knows where the story goes. ↩︎