Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Present-Biased Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply present-biased planning to minimization rather than arbitrary graph navigation, with β<1 | Minimization tasks · Minimization, β<1 | worst-case cost ratio upper bound: ∞ [cite] -> ρ≤k | Mixed |
| Use β>1 instead of β<1 for a minimization task | Present-biased planning · Minimization problems | worst-case cost ratio: unbounded -> β | Helped |
| Use β<1 for a maximization task | Present-biased planning · Maximization problems | worst-case cost ratio: 1/β | Helped |
| Use β>1 for a maximization task | Present-biased planning · Maximization problems | worst-case cost ratio: at least \frac{1}{c}\,β^{c-1}; at most β^c | Hurt |
| Apply present-biased planning to set cover rather than use a generic minimization-task bound | Present-biased planning · d-set cover | α-approximative cost ratio upper bound: α · d·\textsc{opt} | Helped |
| Apply present-biased planning to hitting set rather than set cover | Present-biased planning · d-hitting set | α-approximative cost ratio upper bound: α d!\,(\frac{α}{β}\textsc{opt})^{d} | Mixed |
| Restrict choices to be consistent with previously considered feasible solutions | Present-biased planning · Minimization problems | approximation ratio: α/β | Helped |
| Allow superfluous choices in the set-cover example | Present-biased planning · Set cover | cost ratio: n/c | Hurt |
| Restrict choices to minimal feasible solutions only | Present-biased planning · Set cover instance I_SC^(n) | cost ratio: n/c | No clear effect |
Closest papers by meaning
- Structural Approach to Guiding a Present-Biased Agent (AAAI 2026 · similarity 0.91)
- Analytically Tractable Models for Decision Making under Present Bias (AAAI 2024 · similarity 0.90)
- A Continuous-time Tractable Model for Present-biased Agents (AAAI 2025 · similarity 0.89)
- The Value of Reward Lookahead in Reinforcement Learning (NeurIPS 2024 · similarity 0.85)
- On Shallow Planning Under Partial Observability (AAAI 2025 · similarity 0.85)
- Maximizing utility in multi-agent environments by anticipating the behavior of other learners (NeurIPS 2024 · similarity 0.85)
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