Easy-Options Bias (EOB): Definitions and Implications

• EOB is defined as a phenomenon where systems rely on easily exploited shortcuts instead of engaging with full complexity, observed in machine learning, finance, and behavioral experiments.
• Empirical studies reveal that relying on visible cues or known outcomes can inflate model accuracy or distort risk profiles, as seen in Visual QA benchmarks and option pricing.
• Mitigating EOB requires innovative methods such as generating hard negatives in VQA, applying ease-adjusted utility models in behavioral economics, and using stochastic overlays in financial valuations.

Easy-Options Bias (EOB) is a recurring phenomenon across diverse research domains—machine learning, decision theory, and financial mathematics—where systems or agents exploit shortcuts or default choices that are computationally, perceptually, or behaviorally "easy," thereby skewing model evaluation, risk profiles, or observed preferences. EOB manifests when the apparent performance or decision is artificially inflated by reliance on easily identifiable cues, hidden option value, or known outcomes, rather than fully engaging with the intended complexity or uncertainty in a problem.

1. Formal Definitions and Mathematical Characterizations

Across domains, EOB is rigorously defined via model behavior or value differences under restricted information or altered problem structure.

• In Machine Learning (Visual QA):
  • Let $V$ be a visual input, $Q$ a question, $O = \{o_1, \ldots, o_K\}$ candidate options, $A$ the correct answer, and $\pi(V, Q, O) \to \hat{A} \in O$ a vision-LLM (VLM).
  • Single-model EOB: $Q$ exhibits EOB under model $\pi$ if $\pi(V, O) = A$, i.e., the answer can be recovered from $(V, O)$ without $Q$.
  • Multi-model EOB: Given a family $Q$0 of models, $Q$1 suffers EOB if $Q$2.
  • Total EOB: $Q$3 suffers total EOB if $Q$4 (Zhang et al., 19 Aug 2025).
  • The random-chance baseline for the proportion of questions with EOB—if models guess uniformly randomly—is

  $Q$5

  where $Q$6 controls statistical dependence among models.

• In Financial Mathematics (American Options):

  • EOB quantifies the hidden value difference attributable to convexity in option pricing when stochasticity is added to inputs, e.g., funding or carry rates. For a deterministic rate, value $Q$7; for a stochastic input approximation, value $Q$8:

  $Q$9 - Because $O = \{o_1, \ldots, o_K\}$0 is convex, introducing uncertainty ($O = \{o_1, \ldots, o_K\}$1) always yields a positive EOB (Hassan et al., 15 Feb 2026).

• In Behavioral Economics (Risk Attitude):

  • Given a utility $O = \{o_1, \ldots, o_K\}$2, effective risk aversion with outside option CDF $O = \{o_1, \ldots, o_K\}$3 is

  $O = \{o_1, \ldots, o_K\}$4

  where $O = \{o_1, \ldots, o_K\}$5 is the Arrow–Pratt index. The presence of an "easy" outside option always strictly reduces effective risk aversion (Curello et al., 18 Sep 2025).

2. Empirical Manifestations and Experimental Evidence

EOB has been empirically diagnosed in machine learning, decision experiments, and financial risk valuation.

• Visual QA Benchmarks:

  • Across multiple-choice VQA datasets (MMStar, RealWorldQA, SEED-Bench, Next-QA, STAR, Video-MME), state-of-the-art VLMs (e.g., Qwen2.5VL-7B) can often select the correct answer using only $O = \{o_1, \ldots, o_K\}$6, bypassing $O = \{o_1, \ldots, o_K\}$7. Reported mean accuracy under $O = \{o_1, \ldots, o_K\}$8 is 51.57% vs. 61.11% under $O = \{o_1, \ldots, o_K\}$9, with up to 94% of questions in certain benchmarks exhibiting EOB (e.g., RealWorldQA) (Zhang et al., 19 Aug 2025).
• Behavioral Decision Experiments:
  • In incentivized Ellsberg-type experiments, 54.9% of subjects prefer a risky option with fully known probabilities over a stochastically dominating ambiguous option (the "two-ball Ellsberg paradox"), even when comprehension is controlled. This cannot be explained by standard models (SEU, MEU, α-MEU), as it violates stochastic dominance—subject utility is influenced by the "ease" or transparency of options (Jabarian et al., 2022).
• Option Pricing:
  • Quantifying EOB by introducing stochastic interest rates in American option pricing demonstrates that deterministic-pricer approaches systematically understate the premium, especially for longer maturities or deep-in-the-money options ($A$0 rises monotonically with rate variance) (Hassan et al., 15 Feb 2026).

3. Mechanistic Origins and Structural Drivers

EOB originates from systematic imbalances or features that enable shortcut exploitation, fallbacks, or risk-protecting mechanisms.

• Shortcut in Vision–LLMs:
  • Empirical grounding with CLIP shows that correct options align more closely with visual features than negative options, making simple vision–option similarity maximization sufficient for high accuracy—i.e., models exploit the "vision→options" shortcut (Zhang et al., 19 Aug 2025).
• Outside Options and Risk Attitude:
  • Mathematical analysis proves that endowing the decision-maker with an outside or fallback option always reduces effective risk aversion, explaining increased willingness to select riskier alternatives (e.g., limited liability, money-back guarantees) (Curello et al., 18 Sep 2025).
• EOB in American Options:
  • Convexity of option value with respect to rates means that averaging over the distribution $A$1 yields a higher overall value than the deterministic mean, revealing unpriced optionality in standard methods—i.e., a risk manager using deterministic rates misses the "easy" (hidden) option value (Hassan et al., 15 Feb 2026).

4. Evaluation, Mitigation, and Modeling Strategies

Understanding and correcting for EOB requires both empirical and methodological interventions.

• VQA Benchmark Correction:
  • "GroundAttack" provides a pipeline for generating visually-plausible hard negative options via three stages: (1) a captioner produces a textual scene description, (2) a distractor LLM generates grounded incorrect options, (3) a CLIP-based selector chooses visually similar negatives. This reduces EOB on newly curated sets—post-GroundAttack, $A$2 accuracy drops to near random, and Total EOB approaches the random-chance baseline (3.2%) (Zhang et al., 19 Aug 2025).
• Behavioral Choice Models:
  • Theoretical extensions include ease-adjusted expected utility, where the agent's utility function incorporates an "ease bonus" for computational or transparency simplicity:

  $A$3

  Here, $A$4 measures the ease (e.g., entropy of the outcome distribution), and $A$5 calibrates ease preference (Jabarian et al., 2022).

• Overlay Heuristics in Option Valuation:

  • The stochastic-fugit heuristic overlays classic pricers, allowing "plug-in" correction for EOB using precomputed stopping-time pmf and rate distribution, thus rapidly quantifies hidden option value not captured under deterministic inputs (Hassan et al., 15 Feb 2026).

5. Domain-Specific Implications and Broader Significance

EOB impacts both methodological validity and real-world risk in various research contexts.

• Vision–Language Evaluation:
  • EOB undermines claimed advances in VLM benchmarking by allowing models to exploit "easy" cues. Proper evaluation requires that negative options be visually grounded, and that benchmarks are audited for EOB pre-release (Zhang et al., 19 Aug 2025).
• Risk-Taking and Regulatory Policy:
  • Financial instruments and organizational policies that create de facto outside options (limited liability, bailouts) engender systematic underestimation of risk aversion and may incentivize riskier behaviors than anticipated under standard models (Curello et al., 18 Sep 2025).
• Experimental Economics and Model Foundations:
  • EOB exposes the inadequacy of classical ambiguity aversion theories to explain ease-preference phenomena. Dominated choice of risky acts in paradox designs suggests agents assign positive value to option understandability itself, necessitating re-examination of utility theories (Jabarian et al., 2022).
• Financial Risk Management:
  • Neglecting EOB in American option pricing leads to systematic underestimation of option convexity and premium. Incorporating stochastic input modeling reveals materially higher prices, particularly for longer-duration or deep-in-the-money instruments (Hassan et al., 15 Feb 2026).

6. Recommendations and Future Directions

To mitigate EOB and ensure robust inference or reliable benchmarking:

• Negative options in multiple-choice VQA should always be generated with explicit visual grounding (e.g., via LLM+CLIP pipelines as in GroundAttack).
• Benchmark releases should include EOB-audits using off-the-shelf grounding models.
• Ease or transparency bonuses should be incorporated into behavioral choice models when evaluating preference data.
• Financial valuations should account for EOB by employing stochastic input overlays, especially for products with early-exercise or embedded optionalities.
• Further research should formalize ease-preference in broader classes of non-EU models and extend multi-period and correlated outside-option frameworks (Zhang et al., 19 Aug 2025, Curello et al., 18 Sep 2025, Hassan et al., 15 Feb 2026, Jabarian et al., 2022).