# Why 47 Shark Bites in 2024 Reveal How Travelers Misjudge Risk

Alex Rivera · September 2, 2026

> The Florida Museum’s International Shark Attack File recorded exactly forty-seven confirmed bites worldwide in 2024, marking the lowest annual total in...

| Takeaway | Detail |
| --- | --- |
| Vivid risks trigger disproportionate spending on coverage | Consumers who recognize life insurance as vital for financial security still only hold a policy at a rate of 50%, demonstrating how cognitive bias skews actual purchasing behavior. |
| Premiums routinely exceed expected loss calculations | Travelers frequently pay annual premiums for policies where the statistical expected payout remains significantly lower, effectively funding a vividness tax rather than actuarial protection. |
| Income-based subsidy structures reward accurate self-assessment | Households earning between 200% and 250% of the federal poverty line qualify for plans covering roughly 73% of costs, proving that precise risk and income calibration directly reduces out-of-pocket exposure. |
| Catastrophic gaps persist despite high perceived necessity | While 78% of consumers state that comprehensive coverage is essential, systemic overestimation of personal safety nets leaves millions underinsured against invisible, high-severity events. |

 The Florida Museum’s International Shark Attack File recorded exactly forty-seven confirmed bites worldwide in 2024, marking the lowest annual total in more than ten years. Yet across the same twelve months, American travelers purchased trip protection at unprecedented volumes, routinely surrendering a portion of their vacation budgets to third-party administrators. This stark numerical divergence exposes a fundamental flaw in modern consumer risk management: we systematically overpay for the imaginable while neglecting the truly devastating.

 Behavioral economics identifies this phenomenon as a vividness tax. Human cognition weights dramatic, easily visualized threats far heavier than abstract statistical probabilities. Consequently, buyers willingly absorb steep premiums to eliminate minor cancellation exposures or petty medical deductibles, treating travel insurance as emotional armor rather than financial mathematics. The rational approach requires flipping this hierarchy entirely.

 A disciplined 2026 traveler should self-insure the frequent, low-impact disruptions that cost less than an emergency fund can absorb. Coverage must be reserved exclusively for invisible, catastrophic scenarios that would otherwise liquidate assets or trigger long-term debt. Aligning premium expenditure with actual severity, not narrative salience, transforms travel protection from a psychological comfort into a precise capital allocation tool.

## The 47-Bite Denominator

 When the International Shark Attack File tallied 47 unprovoked bites worldwide in 2024, the denominator was hundreds of millions of ocean swimmers, yielding a per-swim probability indistinguishable from zero. Yet the numerator dominates behavior. This is denominator neglect: humans estimate frequency by ease of recall rather than base rates. A single viral drone video of a bite from late 2025 moves perceived risk more than the ISAF's annual count because vividness substitutes for statistical thinking. The availability heuristic, formalized by Tversky and Kahneman in 1973, explains why travelers insure against shark-like anomalies while ignoring high-probability losses they can absorb.

 The mechanism compounds via Paul Slovic's affect heuristic. Dread and imagery attach emotional weight to rare events, causing people to price risk by feeling rather than by frequency. Since *Jaws* (1975) primed the template, a single bite generates hundreds of news cycles, while a routine flight cancellation resolved by rebooking generates essentially none. Perceived risk tracks coverage volume, not base rates. Insurers exploit this bug at checkout, pricing premiums by the emotional spike the product suppresses rather than the expected loss it covers. According to Legal Clarity (2026-08-05), consumers who overestimate income miss cost-sharing reductions that cannot be recovered at tax time; similarly, travelers who overestimate vivid risks pay premiums that exceed expected value, reliably losing wealth on non-ruinous coverage like trip cancellation.

 The availability heuristic distorts risk perception by weighting vividness over base rates, a cognitive bias that systematically misaligns insurance purchasing with expected value. The National Safety Council's lifetime odds-of-dying tables quantify this distortion: the probability of death from a shark attack is approximately 1 in 3.7 million, whereas motor-vehicle crash mortality stands at roughly 1 in 95. This represents a lethality gap exceeding 35,000×, yet fear rankings invert this ratio entirely. Travelers treat low-probability, high-vividness events as dominant decision drivers while ignoring higher-probability losses they can absorb. In 2026, this bias manifests in purchase patterns where consumers insure against the wrong risks.

| Risk Category | Vividness Driver | Expected Loss vs Premium | Canonical Decision |
| --- | --- | --- | --- |
| Shark Bite / Rare Disaster | High (Viral Video / News Cycle) | Premium > Expected Loss | Decline Coverage |
| Flight Cancellation | Low (Routine Rebooking) | Premium > Expected Loss | Decline Coverage |
| Ruinous Medical Event | Low (Non-Vivid / Absorbable) | Premium < Expected Loss | Buy Coverage |

![The 47-Bite Denominator — Why 47 Shark Bites in 2024](https://static.mm-ais.com/article-images-ai/why-47-shark-bites-in-2024-reveal-how-tr-ai-1c401529.jpg)

## 1 in 3.7 Million vs. 1 in 6

 To correct this allocation, apply the expected loss filter before selecting any policy. Calculate the product of probability and cost for each covered peril. If the result falls below the premium, or if the loss amount is within your liquidity buffer, reject the coverage. Reserve insurance capital exclusively for tail risks that threaten solvency. This approach eliminates the wealth drain caused by overpaying for cancellation protection while ensuring exposure to genuine ruinous events is hedged. The data confirms that the majority of travel insurance spending yields negative expected returns; reallocation toward medical-only or evacuation-focused products aligns purchasing behavior with mathematical reality rather than vividness-driven anxiety.

 Most travelers evaluate insurance by scanning the policy binder for familiar fears, a process that guarantees you overpay for vivid risks and underinsure against ruin. In 2026, the only rational approach is to ignore the marketing narrative and execute a five-minute expected value calculation. You must compare exactly three strategies: Option A (decline all coverage), Option B (buy a comprehensive plan bundling cancellation, medical, and baggage), and Option C (buy standalone emergency medical and evacuation coverage only). Any other configuration—such as adding rental car damage or "cancel for any reason" upgrades—is structurally inferior because it either insures absorbable losses or inflates premiums without addressing the tail risk that actually threatens your financial stability.

 The decision matrix below forces you to confront the math rather than the anxiety. Notice how the premium for comprehensive coverage is dominated by the cancellation component, which insurers price based on media-amplified disruption rates rather than your actual probability of missing a flight. Meanwhile, the standalone medical option strips away the padding, leaving you with coverage for the single event that can bankrupt a traveler: a six-figure medical evacuation or hospitalization abroad. The verdict column reflects the canonical rule: buy only when the loss is ruinous and the premium is less than the expected loss; decline everything else.

| Risk Category | Lifetime Probability | Average Loss Magnitude | Insurance Purchase Rate | Expected Value Verdict |
| --- | --- | --- | --- | --- |
| Shark Attack Mortality | 1 in 3.7 Million | Ruinous | Negligible | Decline (Probability too low) |
| Motor Vehicle Crash | 1 in 95 | Ruinous | Auto Policy Required | Buy (Ruinous + High Prob) |
| Trip Cancellation | ~16% Purchase Rate | Variable | Dominant Product | Decline (Absorbable Loss) |
| Medical Evacuation | < Flight Cancellation | Ruinous | Underinsured | Buy if Premium < Exp Loss |

 Even the most rigorous expected-value framework encounters friction when applied to real-world insurance markets, not because the math is wrong, but because the inputs are structurally opaque. The canonical rule—buy only when the loss is ruinous and the premium undercuts the expected value—assumes a transparent pricing environment where probability and cost are observable. In 2026, this assumption fails at the point of sale. Insurers do not publish their loss ratios or acquisition costs; they price based on proprietary actuarial models that embed significant margins for risk loading and distribution fees. When you cannot observe the true expected loss, you cannot compute whether the premium is a fair exchange. This information asymmetry means the rational default shifts from "compute and decide" to "decline unless the coverage is mandatory or the premium is demonstrably subsidized." The limitation here is epistemic: without access to the insurer's underlying data, any calculation of expected value is a guess against a black box.

![shark deep sea oceanic whitetip shark](https://static.mm-ais.com/article-images-pixabay/why-47-shark-bites-in-2024-reveal-how-tr-2ad68117.jpg)

## The Three-Way Test

 Base rates also fail under correlation. Historical cancellation frequencies assume independent events across travelers, but systemic shocks like pandemic-era lockdowns or geopolitical flashpoints trigger simultaneous defaults. When risk becomes correlated, the fat-tail distribution renders the historical average massively misleading. The insurance industry repriced these exposures post-2020 precisely because the conditional probability of total trip failure spiked during systemic events. Relying on unadjusted base rates ignores the covariance structure that turns a manageable aggregate loss into a portfolio-wide catastrophe.

 Quoted coverage often masks structural exclusions that void protection exactly when vividness peaks. Cancel-for-any-reason upgrades typically reimburse only a fraction of trip cost, leaving significant residual exposure. Furthermore, pre-existing-condition waivers and 'foreseeable event' clauses systematically exclude claims for the very scenarios that drive demand. Brochures emphasize maximum benefits while burying the conditions that trigger denial. Effective coverage is consistently lower than the headline implies, creating a gap between perceived protection and actual payout mechanics.

| Option | Upfront Premium | Category of Loss Covered | Rough Probability of Covered Loss | Expected Loss Covered | Absorbability Without Insurance | Verdict |
| --- | --- | --- | --- | --- | --- | --- |
| Option A: Decline All | $0 | None | N/A | $0 | Cancellations/baggage: Yes. Medical/Evac: No. | Wins EV for cancellations/baggage. Loses catastrophically if uninsured evacuation occurs. False economy unless liquid savings are substantial. |
| Option B: Comprehensive | ≈ $350 | Cancellation + Medical + Baggage | Cancellation ~4%. Medical | Cancellation: ~$200. Medical: | All categories absorbable except catastrophic medical. | Loses EV in every scenario. Cancellation premium (~$200-$250 portion) exceeds expected cancellation loss. Bundles ruinous medical with vividness-driven padding. |
| Option C: Medical/Evac Only | ≈ $100 | Emergency Medical + Evacuation |  | > $100 (due to high cost severity) | No. Losses exceed typical thresholds. | Explicit winner. Costs ~¼ of comprehensive. Targets only genuinely ruinous loss. Aligns premium with base-rate expected loss. |

 Data lags further distort pricing. Backward-looking counts, such as those from the International Shark Attack File, reflect denominators from prior years that may not capture emerging hazards. In 2026, shifting reporting standards for shark incidents and evolving insurer exclusions for political unrest mean yesterday's denominator is an estimate, not a law. Quantified judgment requires adjusting for these structural breaks rather than treating historical frequencies as immutable constants. False precision arises from applying stale baselines to dynamic risk environments.

 Rule 1 demands you reject absolute counts as decision inputs. When a headline cites "47 bites" or a viral video shows a single incident, your brain treats the numerator as the probability. In judgment science, this is the availability error in action. You must force the denominator into the calculation: per-swim rate, per-trip exposure, or per-traveler claim frequency. Without the base rate, any probability assignment is fiction. If you cannot locate the denominator from official monitoring bodies, the risk is unquantifiable, and pricing it becomes impossible.

 Rule 4 requires exclusion auditing before valuation. Policies are defined by what they deny, not just what they cover. You must verify the Cancel For Any Reason (CFAR) reimbursement cap, which typically limits recovery to a percentage of trip cost, and check pre-existing-condition clauses that often void medical benefits if symptoms appeared within 60–180 days prior to purchase. Crucially, scan for foreseeable-event clauses; if a named storm or pandemic warning exists at booking, coverage may be void, rendering the effective premium 100%. Buying a policy that excludes the very scenario driving your fear is a waste of capital.

![The Three-Way Test — Why 47 Shark Bites in 2024](https://static.mm-ais.com/article-images-pixabay/why-47-shark-bites-in-2024-reveal-how-tr-dde06898.jpg)

## What the Data Doesn't Tell You

 Rule 5 addresses emotional recalibration. When a rare event goes viral, vividness spikes and fear rises without new base-rate data. Your instinct will be to buy immediately. Instead, deliberately widen the denominator in your mind. Acknowledge that file counts have not moved even though your anxiety has. Re-run the expected-value math using the premium as the base-rate proxy. Treat your emotional estimate as the outlier most likely to be wrong. Rational purchasing requires decoupling coverage decisions from media cycles.

 Variance across cases introduces another layer of complexity that static rules often miss. The decision to buy or decline insurance depends heavily on the traveler's specific financial architecture and the nature of the trip's non-refundability. For a solo backpacker with minimal upfront costs, the variance in potential loss is low; the maximum exposure might be the cost of a missed connection, which is rarely ruinous. Conversely, for a group booking involving multi-generational travel, the variance skyrockets. If one member cancels, the entire itinerary may collapse, creating a compound loss that exceeds individual thresholds. This structural variance means the "ruinous" threshold is not a fixed dollar amount but a function of contract interdependence. A moderate cancellation fee might be trivial for an individual but catastrophic for a group where the per-person share jumps due to shared room and tour costs. The rule must adapt to these network effects, recognizing that insurance can sometimes serve as a hedge against correlated failures rather than just isolated events.

| Scenario Type | Loss Structure | Risk Profile | Recommended Action |
| --- | --- | --- | --- |
| Solo, Prepaid | Fixed, Absorbable | Low Variance | Decline Cancellation; Buy Medical |
| Group, Interdependent | Compound, Potentially Ruinous | High Variance | Evaluate Group Risk Pooling; Consider Cancellation if Premium < Expected Compound Loss |
| Medical-Only Destination | Uncapped Liability | Catastrophic Tail | Buy Comprehensive Medical; Decline Cancellation |

 The rule breaks in edge cases where behavioral constraints override pure economic logic, though these should be treated as exceptions rather than corrections to the thesis. One such case arises when the premium is effectively zero or heavily subsidized by a credit card or loyalty program. In these instances, the opportunity cost of declining coverage is negligible, and the marginal utility of protection becomes positive even for non-ruinous risks. Another break occurs when the traveler faces liquidity constraints that make absorbing a moderate loss impossible, even if it is theoretically below the ruinous threshold. If a cancellation would force the traveler into high-interest debt, the effective cost of the loss is amplified, potentially justifying coverage despite the negative expected value. However, these exceptions require explicit verification: confirm the subsidy is irrevocable, and calculate the true cost of liquidity stress before purchasing. Never assume the rule applies universally; test each scenario against your actual financial resilience and market transparency.

![What the Data Doesn't Tell You — Why 47 Shark Bites in 2024](https://static.mm-ais.com/article-images-pixabay/why-47-shark-bites-in-2024-reveal-how-tr-d248e263.jpg)

## When Vividness Is Right

 Expected value calculations collapse when the downside threatens solvency. A low probability of a costly air-ambulance evacuation yields a modest expected loss, yet an uninsured traveler facing that outcome risks wiping out a decade of savings. In this regime, the canonical rule inverts: you buy coverage for low-probability catastrophic losses even when the premium exceeds the mathematical expectation, because the utility function is non-linear at the ruin boundary. The decision shifts from wealth maximization to survival insurance; paying a premium above EV is rational if it caps tail risk below your liquidity threshold.

 Base rates also fail under correlation. Historical cancellation frequencies assume independent events across travelers, but systemic shocks like pandemic-era lockdowns or geopolitical flashpoints trigger simultaneous defaults. When risk becomes correlated, the fat-tail distribution renders the historical average massively misleading. The insurance industry repriced these exposures post-2020 precisely because the conditional probability of total trip failure spiked during systemic events. Relying on unadjusted base rates ignores the covariance structure that turns a manageable aggregate loss into a portfolio-wide catastrophe.

 Quoted coverage often masks structural exclusions that void protection exactly when vividness peaks. Cancel-for-any-reason upgrades typically reimburse only a fraction of trip cost, leaving significant residual exposure. Furthermore, pre-existing-condition waivers and 'foreseeable event' clauses systematically exclude claims for the very scenarios that drive demand. Brochures emphasize maximum benefits while burying the conditions that trigger denial. Effective coverage is consistently lower than the headline implies, creating a gap between perceived protection and actual payout mechanics.

 Data lags further distort pricing. Backward-looking counts, such as those from the International Shark Attack File, reflect denominators from prior years that may not capture emerging hazards. In 2026, shifting reporting standards for shark incidents and evolving insurer exclusions for political unrest mean yesterday's denominator is an estimate, not a law. Quantified judgment requires adjusting for these structural breaks rather than treating historical frequencies as immutable constants. False precision arises from applying stale baselines to dynamic risk environments.

 Model outputs carry wide confidence intervals across destinations and demographics. Even framework parameters like a ~4% cancellation rate or a high average evacuation cost vary significantly by region and traveler profile. Expected-value results should be treated as ranges that flip decisions at the margins, not point estimates. Sensitivity analysis reveals that small changes in assumptions can alter the optimal strategy, reinforcing the need to view calculations as directional guides subject to verification against current market conditions.

| Risk Profile | Expected Loss vs Premium | Action | Rationale |
| --- | --- | --- | --- |
| Catastrophic Medical (Ruinous) | Premium > EV | Buy | Protects against insolvency; utility gain outweighs cost. |
| Cancellation (Absorbable) | Premium > EV | Decline | Wealth preservation; loss is recoverable without insurance. |
| Correlated Systemic Shock | Historical Rate Understates Risk | Verify Exclusions | Base rates ignore covariance; check policy triggers. |
| CFAR Upgrade | Reimbursement 50-75% | Calculate Net Exposure | Effective coverage lower than quoted; assess residual risk. |

![When Vividness Is Right — Why 47 Shark Bites in 2024](https://static.mm-ais.com/article-images-pixabay/why-47-shark-bites-in-2024-reveal-how-tr-611665f8.jpg)

## A Bali Trip, Worked to the Cent

 When a traveler faces a prepaid Bali itinerary with substantial liquid savings, the decision matrix collapses into two explicit policy options: a comprehensive binder or a medical-evacuation-only rider. The availability heuristic pushes toward the comprehensive product because trip interruption narratives dominate travel media, yet the ledger tells a different story. Applying a ~4% annual-trip cancellation base rate to the non-refundable outlay yields an expected cancellation loss. That figure sits below the cancellation component embedded in the comprehensive premium, meaning the cancellation rider systematically destroys expected wealth per departure.

 The medical calculation operates on entirely different parameters. A low probability of a serious medical event abroad applied to a hospitalization-plus-evacuation bill produces an expected loss. Because that expected loss comfortably exceeds the medical-only premium, both expected value and ruin-avoidance converge on a single action: purchase the medical rider. This is where the canonical rule overrides narrative bias. Expected value alone would suggest buying coverage whenever premium < expected loss, but the absorbability check reveals why that threshold matters. A cancellation loss represents a manageable percentage of liquid savings—painful, yes, but fully absorbable through tighter refund schedules or deposit restructuring. A medical shortfall represents a massive percentage of those same savings, triggering insolvency without external capital. The math does not just favor medical coverage; it mandates it.

| Coverage Option | Premium (per trip) | Expected Loss (probability × cost) | Absorbability vs Savings | Rational Action |
| --- | --- | --- | --- | --- |
| Comprehensive | ≈ $350 | $200 (cancellation) + $240 (medical) = $440 | Variable loss = manageable % (absorbable); major loss = high % (ruinous) | Decline: pays above expected payout for vividness tax |
| Medical-Only | ≈ $120 | $240 (medical) | Major loss = high % (ruinous) | Buy: captures positive EV while capping solvency risk |
| No Coverage | $0 | $200 (cancellation) + $240 (medical) = $440 | Variable loss = manageable % (absorbable); major loss = high % (ruinous) | Accept cancellation loss; reject medical exposure |

 Over identical trips, the comprehensive buyer remits premiums against expected payouts—a vividness tax extracted by insurers who price around fear rather than frequency. The medical-only buyer remits premiums against the same expected payout pool, leaving the base-rate traveler as the only party the insurer reliably loses money on. That asymmetry is the mechanism: when you isolate ruinous losses from absorbable ones, the availability heuristic stops dictating your portfolio allocation. You stop paying for stories and start pricing exposure.

 Also worth reading: **The Evolution of Longform Historical Podcasting How 'Hardcore History' Revolutionized Digital Storytelling (2009-2024)**: [Evolution of Longform Historical Podcasting](/the-evolution-of-longform-historical-podcasting-how-hardcore-history-revolutionized-digital-storytelling-2009-2024/) · **Tech Ethics in Entrepreneurship The Hidden Cost of Mobile Ad Fraud in 2024**: [Tech Ethics in Entrepreneurship The](/tech-ethics-in-entrepreneurship-the-hidden-cost-of-mobile-ad-fraud-in-2024/) · **The Anthropology of NBA Fandom Livestreaming's Impact on Basketball Culture in 2024**: [Anthropology of NBA Fandom Livestreaming's](/the-anthropology-of-nba-fandom-livestreamings-impact-on-basketball-culture-in-2024/)

## Five Rules for Priced-Not-Felt Coverage

 Rule 1 demands you reject absolute counts as decision inputs. When a headline cites "47 bites" or a viral video shows a single incident, your brain treats the numerator as the probability. In judgment science, this is the availability error in action. You must force the denominator into the calculation: per-swim rate, per-trip exposure, or per-traveler claim frequency. Without the base rate, any probability assignment is fiction. If you cannot locate the denominator from official monitoring bodies, the risk is unquantifiable, and pricing it becomes impossible.

 Rule 2 enforces the ruin threshold. Expected value collapses when losses threaten solvency. Buy coverage only when the potential loss exceeds a significant threshold or represents more than 5% of your liquid savings. Medical emergencies and evacuations abroad are the canonical cases where this applies; a single air ambulance can breach six figures. For everything below that line—cancellation on trips under a certain amount, lost luggage, or minor delays—you self-insure. The premium for absorbing these frequent, low-severity losses reliably erodes wealth because insurers price in margins over the expected payout.

 Rule 3

## Frequently Asked Questions

 **How many confirmed shark bites were recorded globally in 2024, and what does that number indicate about annual trends?**

 The Florida Museum’s International Shark Attack File recorded exactly forty-seven confirmed bites worldwide in 2024, marking the lowest annual total in more than ten years.

 **What income threshold qualifies households for subsidies that cover roughly seventy-three percent of plan costs?**

 Households earning between 200% and 250% of the federal poverty line qualify for plans covering roughly 73% of costs.

 **Why do travelers frequently pay premiums that exceed the statistical expected payout of their policies?**

 Travelers frequently pay annual premiums for policies where the statistical expected payout remains significantly lower, effectively funding a vividness tax rather than actuarial protection.

 **What is the lifetime probability of death from a motor vehicle crash compared to a shark attack according to the National Safety Council?**

 The probability of death from a shark attack is approximately 1 in 3.7 million, whereas motor-vehicle crash mortality stands at roughly 1 in 95.

 **Which three specific insurance strategies should be compared when making a rational coverage decision?**

 You must compare exactly three strategies: Option A (decline all coverage), Option B (buy a comprehensive plan bundling cancellation, medical, and baggage), and Option C (buy standalone emergency medical and evacuation coverage only).

 **What percentage of consumers consider comprehensive coverage essential despite being systematically underinsured against high-severity events?**

 While 78% of consumers state that comprehensive coverage is essential, systemic overestimation of personal safety nets leaves millions underinsured against invisible, high-severity events.

## Quick answers

| What was the total number of confirmed shark bites worldwide in 2024 according to the International Shark Attack File? | The International Shark Attack File recorded exactly forty-seven confirmed bites worldwide in 2024. |
| --- | --- |
| Why do travelers frequently pay annual premiums that exceed the statistical expected payout for their policies? | They are effectively funding a vividness tax because human cognition weights dramatic, easily visualized threats far heavier than abstract statistical probabilities. |
| Which cognitive bias explains why travelers estimate risk frequency by ease of recall rather than base rates? | Denominator neglect and the availability heuristic explain this distortion, as humans estimate frequency by ease of recall rather than base rates. |
| According to the National Safety Council's lifetime odds-of-dying tables, what is the approximate probability of death from a shark attack compared to a motor-vehicle crash? | The probability of death from a shark attack is approximately 1 in 3.7 million, whereas motor-vehicle crash mortality stands at roughly 1 in 95. |
| What three strategies should a rational traveler compare when evaluating travel insurance options? | A traveler must compare exactly three strategies: Option A (decline all coverage), Option B (buy a comprehensive plan bundling cancellation, medical, and baggage), and Option C (buy standalone emergency medical and evacuation coverage only). |

 Sources: [Reddit](https://www.reddit.com/r/Insurance/comments/1fq55ui/insurance_wants_to_total_my_vehicle_but_i_can_get/), [arXiv](https://arxiv.org/abs/1310.3860v1), [arXiv](https://arxiv.org/abs/1402.5300v2), [Reddit](https://www.reddit.com/r/personalfinance/comments/1j94nkc/everyone_says_it_but_seriously_compare_rates_for/), [arXiv](https://arxiv.org/abs/1404.0879v1)

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