# Aspirations and wants *Mechanic* · `aspiration-wants` · status: **sourced** An agent's long-term aspiration and current state generate short-horizon wants, and sometimes fears, whose fulfillment pays into a reward meter or currency. | field | value | |---|---| | aliases | wants and fears, wishes, whims, life goals | | family | Agents & behavior | | substrate | Agent-based, Rule system, Stochastic events | | math basis | Utility / decision theory, Markov / stochastic processes | | feedback | Reinforcing (positive) | | player interface | Observation only, Choice / cards | | legibility | 4 | | emergence potential | 3 | | authoring cost | 3 | | compute cost | 1 | | tuning difficulty | 3 | | ai leverage | 4 | | maturity | Established | | core count *(computed)* | 1 | | earliest game *(computed)* | [The Sims 2](../game/the-sims-2.md) | | earliest year *(computed)* | 2004 | | prevalence *(computed)* | 3 | | prevalence rank *(computed)* | 156 | | prevalence share *(computed)* | 0.017 | | second game *(computed)* | [The Sims 3](../game/the-sims-3.md) | | second year *(computed)* | 2009 | | subgenre spread *(computed)* | 1 | | technique count *(computed)* | 1 | | technique exposure *(computed)* | 0.75 | ## Definition Owns, per agent, a long-term aspiration (a goal type such as family, knowledge, or fortune), a few open want slots, sometimes paired with fear slots, and a reward stock. A generator refills empty slots by sampling wants from a pool weighted by aspiration, traits, life stage, needs, and recent events; each want is a predicate over simulation state (make a friend, gain a skill level, get promoted) with a point value. When a predicate becomes true the want pays its points and leaves its slot, and a realized fear subtracts. Points feed a meter that shifts mood or behavior and usually a currency for rewards. The Sims 2 set the form: the aspiration biases which wants and fears appear, fulfilled wants fill the aspiration meter, and an elder's remaining lifespan depends on that meter. The Sims 3 replaced it with wishes that pay Lifetime Happiness, and The Sims 4 ties a bonus trait and a goal track to the chosen aspiration. Wants do not decay like needs; unlike a career ladder they are generated from state rather than authored as a fixed sequence, and unlike villager requests they are the agent's own goals. ## Player Experience A stream of small, personal goals. The player reads what each character wants right now, plans the day around one or two of them, and collects a payout when they land, so an open sandbox gains a to-do list that comes from the character rather than the designer. Fears add dread: the player steers away from events the character would hate. ## Design Levers - number of open slots and whether fears exist alongside wants - reroll, lock, and dismiss rules for slots - generator weights by aspiration, traits, life stage, and recent events - point value per want and what points buy (meter effects, reward objects, traits) - difficulty and time horizon of individual wants relative to play pace - whether unmet wants cost anything or simply expire ## Failure Modes - Wants that need unavailable or off-lot actions clutter the slots and teach players to ignore the panel. - Cheap repeatable wants let players farm points, so the reward store empties early and the meter stops mattering. - Generators that ignore context produce incoherent wants, such as wanting a first date the day after a spouse dies, which breaks the illusion of a mind. - Too many slots turn the panel into a checklist with no choice in it. ## Dynamics - Players chase wants that line up across household members, so generated goals couple separate characters' days. - Wants seeded by recent events (a promotion, a new baby) create follow-on arcs, an informal story chain driven by state. - Points buy rewards that make later wants easier to meet, a reinforcing loop toward a comfortable late game. ## Open Questions - Can a language model write wants from an agent's memories and relationships while keeping each want checkable as a predicate over game state? - Do fears add enough drama to justify goals the player cannot fully prevent from failing? ## Ratings - legibility = 4 (run `2026-10-24-taxonomy-triage-a`): original score - legibility = 4 (run `2026-10-29-shreyko-antim-rate-scores-19`): Visible goals and point payouts make fulfillment mostly intuitive. State-weighted rerolls and fears bring occasional surprises below 5, without needing deliberate study to grasp the goal-reward loop (3). - emergence_potential = 3 (run `2026-10-24-taxonomy-triage-a`): original score - emergence_potential = 4 (run `2026-10-29-shreyko-antim-rate-scores-19`): Event-seeded wants lead to follow-on arcs, align household plans and feed reward loops. These are consequence chains across systems above occasional unplanned situations (3), while not every wants system becomes a primary story engine (5). - authoring_cost = 3 (run `2026-10-24-taxonomy-triage-a`): original score - authoring_cost = 3 (run `2026-10-29-shreyko-antim-rate-scores-19`): Each want needs a contextual predicate, text, eligibility and reward, moderate bespoke content above small tables (2). Recombining a pool avoids requiring a large voice or animation pipeline for each generated goal (4). - compute_cost = 1 (run `2026-10-24-taxonomy-triage-a`): original score - compute_cost = 2 (run `2026-10-29-shreyko-antim-rate-scores-19`): Slot sampling and predicate evaluation scale linearly over agents and a bounded pool with modest constants. This exceeds negligible single-value updates (1), but is not a usual late-game slowdown source (3). - tuning_difficulty = 3 (run `2026-10-24-taxonomy-triage-a`): original score - tuning_difficulty = 4 (run `2026-10-29-shreyko-antim-rate-scores-19`): Cheap repeatable wants and reward feedback can create dominant farming strategies after small weight or payout changes. This is more sensitive than ordinary playtesting (3), while eligibility and repeat limits allow balance to settle below 5. - ai_leverage = 4 (run `2026-10-24-taxonomy-triage-a`): original score - ai_leverage = 4 (run `2026-10-29-shreyko-antim-rate-scores-19`): Grounded models could propose contextually coherent personal goals and evolving arcs outside a fixed authored pool, enabling substantially new forms beyond cheaper variants (3). State-generated goals and fulfillment rewards still define the mechanic, excluding 5. ## Backlinks (computed) **affected by:** [Generative agents (memory stream, reflection, planning)](../technique/generative-agents.md) **used by:** [The Sims 2](../game/the-sims-2.md), [The Sims 3](../game/the-sims-3.md), [The Sims 4](../game/the-sims-4.md) ## Claims - The Sims 2 gives each Sim an aspiration with wants and fears, and an elder's remaining lifespan depends on the aspiration meter; The Sims 3 replaced wants and fears with wishes that pay Lifetime Happiness. *(confidence: medium)* [[86](../source/src-wikipedia-the-sims-2.md), [87](../source/src-wikipedia-the-sims-3.md)] - The Sims 4 adults take a bonus trait from their chosen aspiration. *(confidence: medium)* [[88](../source/src-wikipedia-the-sims-4.md)] ## Sources - [The Sims 2](../source/src-wikipedia-the-sims-2.md) - [The Sims 3](../source/src-wikipedia-the-sims-3.md) - [The Sims 4](../source/src-wikipedia-the-sims-4.md) --- Provenance: created in run `2026-10-24-taxonomy-triage-a`; reviewed in `2026-10-29-shreyko-antim-rate-scores-19`.