Datasets:
pretty_name: STEER
license: cc-by-4.0
language:
- en
task_categories:
- multiple-choice
- question-answering
tags:
- economics
- rationality
- decision-making
- game-theory
- social-choice
- reasoning
- benchmark
size_categories:
- 10K<n<100K
configs:
- config_name: default
default: true
data_files:
- split: test
path: data/*/test.parquet
- split: few_shot
path: data/*/few_shot.parquet
- config_name: add_sub
data_files:
- split: test
path: data/add_sub/test.parquet
- split: few_shot
path: data/add_sub/few_shot.parquet
- config_name: mult_div
data_files:
- split: test
path: data/mult_div/test.parquet
- split: few_shot
path: data/mult_div/few_shot.parquet
- config_name: compute_expectations
data_files:
- split: test
path: data/compute_expectations/test.parquet
- split: few_shot
path: data/compute_expectations/few_shot.parquet
- config_name: micro_utility
data_files:
- split: test
path: data/micro_utility/test.parquet
- split: few_shot
path: data/micro_utility/few_shot.parquet
- config_name: optimize_continuous
data_files:
- split: test
path: data/optimize_continuous/test.parquet
- split: few_shot
path: data/optimize_continuous/few_shot.parquet
- config_name: constrained_optimization
data_files:
- split: test
path: data/constrained_optimization/test.parquet
- split: few_shot
path: data/constrained_optimization/few_shot.parquet
- config_name: prob_basic
data_files:
- split: test
path: data/prob_basic/test.parquet
- split: few_shot
path: data/prob_basic/few_shot.parquet
- config_name: complement_rule
data_files:
- split: test
path: data/complement_rule/test.parquet
- split: few_shot
path: data/complement_rule/few_shot.parquet
- config_name: bayes_rule
data_files:
- split: test
path: data/bayes_rule/test.parquet
- split: few_shot
path: data/bayes_rule/few_shot.parquet
- config_name: categorical_syllogism
data_files:
- split: test
path: data/categorical_syllogism/test.parquet
- split: few_shot
path: data/categorical_syllogism/few_shot.parquet
- config_name: conditional_syllogism
data_files:
- split: test
path: data/conditional_syllogism/test.parquet
- split: few_shot
path: data/conditional_syllogism/few_shot.parquet
- config_name: equiv_contrapositive
data_files:
- split: test
path: data/equiv_contrapositive/test.parquet
- split: few_shot
path: data/equiv_contrapositive/few_shot.parquet
- config_name: first_order_false_belief
data_files:
- split: test
path: data/first_order_false_belief/test.parquet
- split: few_shot
path: data/first_order_false_belief/few_shot.parquet
- config_name: second_order_false_belief
data_files:
- split: test
path: data/second_order_false_belief/test.parquet
- split: few_shot
path: data/second_order_false_belief/few_shot.parquet
- config_name: completeness
data_files:
- split: test
path: data/completeness/test.parquet
- split: few_shot
path: data/completeness/few_shot.parquet
- config_name: transitivity
data_files:
- split: test
path: data/transitivity/test.parquet
- split: few_shot
path: data/transitivity/few_shot.parquet
- config_name: independence
data_files:
- split: test
path: data/independence/test.parquet
- split: few_shot
path: data/independence/few_shot.parquet
- config_name: sunk_cost
data_files:
- split: test
path: data/sunk_cost/test.parquet
- split: few_shot
path: data/sunk_cost/few_shot.parquet
- config_name: endowment_effect
data_files:
- split: test
path: data/endowment_effect/test.parquet
- split: few_shot
path: data/endowment_effect/few_shot.parquet
- config_name: time_inconsistency
data_files:
- split: test
path: data/time_inconsistency/test.parquet
- split: few_shot
path: data/time_inconsistency/few_shot.parquet
- config_name: completeness_risk
data_files:
- split: test
path: data/completeness_risk/test.parquet
- split: few_shot
path: data/completeness_risk/few_shot.parquet
- config_name: transitivity_risk
data_files:
- split: test
path: data/transitivity_risk/test.parquet
- split: few_shot
path: data/transitivity_risk/few_shot.parquet
- config_name: independence_risk
data_files:
- split: test
path: data/independence_risk/test.parquet
- split: few_shot
path: data/independence_risk/few_shot.parquet
- config_name: compute_expected_utility
data_files:
- split: test
path: data/compute_expected_utility/test.parquet
- split: few_shot
path: data/compute_expected_utility/few_shot.parquet
- config_name: maximize_expected_utility
data_files:
- split: test
path: data/maximize_expected_utility/test.parquet
- split: few_shot
path: data/maximize_expected_utility/few_shot.parquet
- config_name: loss_aversion
data_files:
- split: test
path: data/loss_aversion/test.parquet
- split: few_shot
path: data/loss_aversion/few_shot.parquet
- config_name: gamblers_fallacy
data_files:
- split: test
path: data/gamblers_fallacy/test.parquet
- split: few_shot
path: data/gamblers_fallacy/few_shot.parquet
- config_name: certainty_effect
data_files:
- split: test
path: data/certainty_effect/test.parquet
- split: few_shot
path: data/certainty_effect/few_shot.parquet
- config_name: reflection_effect
data_files:
- split: test
path: data/reflection_effect/test.parquet
- split: few_shot
path: data/reflection_effect/few_shot.parquet
- config_name: ambiguity_aversion
data_files:
- split: test
path: data/ambiguity_aversion/test.parquet
- split: few_shot
path: data/ambiguity_aversion/few_shot.parquet
- config_name: interpret_games
data_files:
- split: test
path: data/interpret_games/test.parquet
- split: few_shot
path: data/interpret_games/few_shot.parquet
- config_name: best_response
data_files:
- split: test
path: data/best_response/test.parquet
- config_name: dominant_strategies
data_files:
- split: test
path: data/dominant_strategies/test.parquet
- split: few_shot
path: data/dominant_strategies/few_shot.parquet
- config_name: dominated_strategies
data_files:
- split: test
path: data/dominated_strategies/test.parquet
- split: few_shot
path: data/dominated_strategies/few_shot.parquet
- config_name: iterated_removal
data_files:
- split: test
path: data/iterated_removal/test.parquet
- config_name: pure_nash
data_files:
- split: test
path: data/pure_nash/test.parquet
- split: few_shot
path: data/pure_nash/few_shot.parquet
- config_name: backward_induction
data_files:
- split: test
path: data/backward_induction/test.parquet
- split: few_shot
path: data/backward_induction/few_shot.parquet
- config_name: subgame_nash
data_files:
- split: test
path: data/subgame_nash/test.parquet
- split: few_shot
path: data/subgame_nash/few_shot.parquet
- config_name: feasibility
data_files:
- split: test
path: data/feasibility/test.parquet
- split: few_shot
path: data/feasibility/few_shot.parquet
- config_name: enforceability
data_files:
- split: test
path: data/enforceability/test.parquet
- split: few_shot
path: data/enforceability/few_shot.parquet
- config_name: trigger
data_files:
- split: test
path: data/trigger/test.parquet
- split: few_shot
path: data/trigger/few_shot.parquet
- config_name: bayes_nash
data_files:
- split: test
path: data/bayes_nash/test.parquet
- split: few_shot
path: data/bayes_nash/few_shot.parquet
- config_name: pareto_sc
data_files:
- split: test
path: data/pareto_sc/test.parquet
- split: few_shot
path: data/pareto_sc/few_shot.parquet
- config_name: sc_monotonicity_rules
data_files:
- split: test
path: data/sc_monotonicity_rules/test.parquet
- split: few_shot
path: data/sc_monotonicity_rules/few_shot.parquet
- config_name: sc_transitivity_rules
data_files:
- split: test
path: data/sc_transitivity_rules/test.parquet
- split: few_shot
path: data/sc_transitivity_rules/few_shot.parquet
- config_name: sc_nondictatorship
data_files:
- split: test
path: data/sc_nondictatorship/test.parquet
- split: few_shot
path: data/sc_nondictatorship/few_shot.parquet
- config_name: plurality_voting
data_files:
- split: test
path: data/plurality_voting/test.parquet
- split: few_shot
path: data/plurality_voting/few_shot.parquet
- config_name: borda_count
data_files:
- split: test
path: data/borda_count/test.parquet
- split: few_shot
path: data/borda_count/few_shot.parquet
- config_name: copeland_method
data_files:
- split: test
path: data/copeland_method/test.parquet
- split: few_shot
path: data/copeland_method/few_shot.parquet
- config_name: fair_division
data_files:
- split: test
path: data/fair_division/test.parquet
- split: few_shot
path: data/fair_division/few_shot.parquet
- config_name: condorcet_criterion
data_files:
- split: test
path: data/condorcet_criterion/test.parquet
- split: few_shot
path: data/condorcet_criterion/few_shot.parquet
- config_name: dsic_mechanism
data_files:
- split: test
path: data/dsic_mechanism/test.parquet
- split: few_shot
path: data/dsic_mechanism/few_shot.parquet
- config_name: bic_mechanism
data_files:
- split: test
path: data/bic_mechanism/test.parquet
- split: few_shot
path: data/bic_mechanism/few_shot.parquet
- config_name: ir_mechanism
data_files:
- split: test
path: data/ir_mechanism/test.parquet
- split: few_shot
path: data/ir_mechanism/few_shot.parquet
- config_name: budget_balance
data_files:
- split: test
path: data/budget_balance/test.parquet
- split: few_shot
path: data/budget_balance/few_shot.parquet
- config_name: top_trading
data_files:
- split: test
path: data/top_trading/test.parquet
- split: few_shot
path: data/top_trading/few_shot.parquet
- config_name: auctions_risk
data_files:
- split: test
path: data/auctions_risk/test.parquet
- split: few_shot
path: data/auctions_risk/few_shot.parquet
- config_name: auctions_affiliated
data_files:
- split: test
path: data/auctions_affiliated/test.parquet
- split: few_shot
path: data/auctions_affiliated/few_shot.parquet
STEER
STEER measures how well large language models reason about economic decisions, from arithmetic and probability through single-agent choice under certainty and risk, strategic reasoning in games, and decisions made on behalf of others (social choice and mechanism design). Each element of the paper's taxonomy is one skill, for example applying Bayes' rule or finding a pure-strategy Nash equilibrium; some elements test for behavioural biases (certainty effect, sunk cost, endowment effect) by checking that related answers are consistent.
- Paper: STEER: Assessing the Economic Rationality of Large Language Models (ICML 2024), 2402.09552
- Website, with element pages and sample questions: https://steer-benchmark.cs.ubc.ca/steer
- Evaluation code: narunraman/STEER
The companion benchmark STEER-ME is narunraman/steer_me.
Contents
58 elements; test has 16,300 questions (20,735 rows), few_shot 705 questions (805 rows). A row is one question part: most questions have one part; consistency-graded questions have several.
Loading
from datasets import load_dataset
ds = load_dataset("narunraman/steer", "add_sub") # one element: splits test and few_shot
everything = load_dataset("narunraman/steer") # all elements (config "default")
There is one config per element, named after the element, and a default config with all of them. Every row carries its setting and module, so a setting or module is a filter: everything["test"].filter(lambda r: r["module"] == "...").
How the questions are generated
Each element has a set of question templates written for it, which vary the question type, the domain (subject matter of the story), the difficulty and, where relevant, the perspective (first person, a named third person, ...). A filler program for the element draws the numbers or other content for a template, computes the correct option, and builds the distractors from typical mistakes; options are then shuffled (unless an option refers to others by position, such as 'none of the above'). Every question is determined by element, template_id and seed, so it can be regenerated. This release was generated by autosteer@0.1.0+4bf3f71; ELEMENTS.md describes each element's templates, answer key and distractors.
Sample size: a cell is one combination of type, domain and difficulty within an element. Each cell has 20 test questions, and every element has at least 200 (when there are fewer than 10 cells, the floor is spread evenly over them). Questions that fail automatic checks (no or several correct options, duplicate options, unfilled placeholders) or repeat an earlier question are discarded and redrawn.
Splits
test: the benchmark.few_shot: 5 questions per question type of each element, for use as in-context examples. They come from templates that are never used fortest: the element's own example templates where it has them, otherwise templates held out oftest(only templates whose removal leaves every test cell covered). Nofew_shotquestion or template text occurs intest, and nofew_shottemplate has the text of a template used fortest. 2 elements have nofew_shotsplit because each of their templates is the only one of some test cell:best_response,iterated_removal.
Fields
| field | type | meaning |
|---|---|---|
element |
string | element id; also the config name |
question_id |
string | <element>/<base_id>/<part>, unique in the dataset |
base_id |
string | one question (16 hex digits of a content hash); shared by its parts |
part |
int32 | 0-based part index within base_id; most questions have one part |
template_id |
string | <element>/<pool>/<n>: the template the question was filled from |
question_text |
string | the prompt |
options |
list | answer options in presentation order, without letter labels |
grading |
string | answer or consistency |
correct_index |
int32 | 0-based index of the correct option; null for consistency-graded rows |
consistency_group |
string | rows scored together (<element>/<base_id>); null for answer-graded rows |
consistency_rule |
string | <rule_id>:<allowed>; null for answer-graded rows |
type |
string | question type within the element (default if the element has one) |
domain |
string | subject matter of the wording (e.g. consumer_goods) |
perspective |
string | first_person, second_person, anon, man, woman, ai_assistant; null if none |
tags |
list | other template descriptors (may be empty) |
difficulty |
int32 | difficulty level within the element: 1 is its easiest, up to its number of levels; not comparable across elements |
setting |
string | setting of the taxonomy |
module |
string | module of the taxonomy |
generator |
string | autosteer@<version>+<commit> that generated the row |
seed |
int64 | with element and template_id, regenerates the question |
difficulty is relative to the element: its levels are numbered from 1 (the element's easiest) up to its number of levels, and many elements have a single level. Levels can be compared within an element, not across elements.
Grading
Answer-graded rows (grading == "answer") have one correct option, options[correct_index]. The score of an element is accuracy over its questions; a model's answer is the index of the option it chooses.
Consistency-graded rows (grading == "consistency") have no correct option. They test for a behavioural bias: the parts of one question (rows with the same consistency_group) are presented separately, and the choices count as consistent if the tuple of chosen option indices, in part order, is one of the allowed tuples listed in consistency_rule (<rule_id>:<allowed>, tuples written as digit strings separated by commas). The score is the share of consistent groups.
def consistent(choices, rule): # choices: chosen option index per part, in part order
allowed = rule.split(":", 1)[1].split(",")
return "".join(map(str, choices)) in allowed
Example: same_brand:02,11 means choosing option 0 in part 0 and option 2 in part 1, or option 1 in both, is consistent.
pairwise_complete (completeness, completeness_risk) is the exception: its comma-separated tokens are not allowed tuples but one token per part, in part order, of six digits abpqic: the part compares alternatives a and b (0-based, in the order the question describes them), and p, q, i, c are the option indices of "prefer a", "prefer b", "indifferent" and "cannot decide". The group is consistent (complete) if no part's choice is its c; the transitivity of the answered pairs is a secondary measure (autosteer.release.consistency.score_transitivity).
The rules:
| rule | elements | consistent when |
|---|---|---|
common_ratio_same_side |
certainty_effect (type two_part) |
the sure option and its scaled-down version, or the lottery in both parts. |
ellsberg_seu |
ambiguity_aversion |
the pairs that one subjective probability can explain (the known-odds bet in exactly one part, or indifferent in both). |
mirror_trade |
endowment_effect |
Yes in exactly one part, or indifferent in both. |
pairwise_complete |
completeness, completeness_risk |
no pair answered "cannot decide" (the answers are complete). Reported besides: whether the answered pairs are transitive. |
reflection_same_side |
reflection_effect (type two_part) |
the same side in both parts. |
same_brand |
independence |
the same brand in both parts, or no preference in both. |
In type stated_prior the question states which option the reader chose in an earlier, related choice; the keyed option is the one on the same side.
Elements
Per setting, in paper order. ELEMENTS.md has the full description of every element (how its questions are generated, what varies, how the answer and distractors are computed, how it is graded); elements.csv and elements.jsonl have the same information for programs.
Foundations
| id | config | element | grading | test questions |
|---|---|---|---|---|
| 1.1.a | add_sub |
Addition and Subtraction | answer | 200 |
| 1.1.b | mult_div |
Multiplication and Division | answer | 240 |
| 1.1.c | compute_expectations |
Compute Expectations | answer | 200 |
| 1.2.a | micro_utility |
Optimize over a Discrete Set | answer | 200 |
| 1.2.b | optimize_continuous |
Optimize a Continuous Function | answer | 200 |
| 1.2.c | constrained_optimization |
Constrained Optimization | answer | 240 |
| 1.3.a | prob_basic |
Compute Probabilities of Outcomes | answer | 280 |
| 1.3.b | complement_rule |
Complement Rule | answer | 200 |
| 1.3.c | bayes_rule |
Bayes' Rule | answer | 200 |
| 1.4.a | categorical_syllogism |
Categorical Syllogism | answer | 200 |
| 1.4.b | conditional_syllogism |
Conditional Syllogism | answer | 640 |
| 1.4.c | equiv_contrapositive |
Logical Equivalence of Contrapositive | answer | 200 |
| 1.5.a | first_order_false_belief |
First-Order False Belief | answer | 320 |
| 1.5.b | second_order_false_belief |
Second-Order False Belief | answer | 320 |
Decisions in Single-Agent Environments
| id | config | element | grading | test questions |
|---|---|---|---|---|
| 2.1.a | completeness |
Completeness | consistency | 240 |
| 2.1.b | transitivity |
Transitivity | answer | 200 |
| 2.1.c | independence |
Independence | consistency | 200 |
| 2.2.a | sunk_cost |
Avoidance of the Sunk Cost Fallacy | answer | 320 |
| 2.2.b | endowment_effect |
Avoidance of the Endowment Effect | consistency | 200 |
| 2.2.c | time_inconsistency |
Avoidance of Time Inconsistency | answer | 200 |
| 2.3.a | completeness_risk |
Completeness over Lotteries | consistency | 240 |
| 2.3.b | transitivity_risk |
Transitivity over Lotteries | answer | 200 |
| 2.3.c | independence_risk |
Independence over Lotteries | answer | 200 |
| 2.4.a | compute_expected_utility |
Compute Expected Utility | answer | 200 |
| 2.4.b | maximize_expected_utility |
Maximize Expected Utility | answer | 200 |
| 2.4.e | loss_aversion |
Avoidance of Loss Averse Behavior | answer | 200 |
| 2.5.a | gamblers_fallacy |
Avoidance of the Gambler's Fallacy | answer | 200 |
| 2.5.b | certainty_effect |
Avoidance of the Certainty Effect | mixed | 200 |
| 2.5.c | reflection_effect |
Avoidance of the Reflection Effect | mixed | 200 |
| 2.5.d | ambiguity_aversion |
Avoidance of Ambiguity Aversion | consistency | 200 |
Decisions in Multi-Agent Environments
| id | config | element | grading | test questions |
|---|---|---|---|---|
| 3.1.a | interpret_games |
Interpret Games | answer | 200 |
| 3.1.b | best_response |
Best Response | answer | 200 |
| 3.1.c | dominant_strategies |
Dominant Strategies | answer | 200 |
| 3.1.d | dominated_strategies |
Avoidance of Dominated Strategies | answer | 200 |
| 3.1.e | iterated_removal |
Iterated Removal of Dominated Strategies | answer | 240 |
| 3.1.f | pure_nash |
Pure Nash Equilibrium | answer | 200 |
| 3.2.a | backward_induction |
Backward Induction | answer | 200 |
| 3.2.b | subgame_nash |
Subgame-Perfect Nash Equilibrium | answer | 480 |
| 3.4.a | feasibility |
Feasibility in Infinitely Repeated Games | answer | 240 |
| 3.4.b | enforceability |
Enforceability in Infinitely Repeated Games | answer | 200 |
| 3.4.c | trigger |
Trigger Strategies | answer | 200 |
| 3.5.a | bayes_nash |
Bayes-Nash Equilibrium | answer | 200 |
Decisions on Behalf of Other Agents
| id | config | element | grading | test questions |
|---|---|---|---|---|
| 4.1.a | pareto_sc |
Pareto Efficiency in Social Welfare Functions | answer | 240 |
| 4.1.b | sc_monotonicity_rules |
Monotonicity in Social Welfare Functions | answer | 240 |
| 4.1.c | sc_transitivity_rules |
Transitivity in Social Welfare Functions | answer | 240 |
| 4.1.d | sc_nondictatorship |
Non-Dictatorial Social Welfare Function | answer | 360 |
| 4.2.a | plurality_voting |
Plurality Vote | answer | 200 |
| 4.2.b | borda_count |
Borda Count | answer | 200 |
| 4.2.c | copeland_method |
Copeland's Method | answer | 480 |
| 4.2.d | fair_division |
Fair Division Algorithms | answer | 200 |
condorcet_criterion |
Condorcet Winner | answer | 200 | |
| 4.3.a | dsic_mechanism |
Dominant Strategy Incentive Compatibility | answer | 380 |
| 4.3.b | bic_mechanism |
Bayesian Incentive Compatibility | answer | 800 |
| 4.3.c | ir_mechanism |
Individual Rationality | answer | 640 |
| 4.3.d | budget_balance |
Budget Balanced | answer | 480 |
| 4.4.a | top_trading |
Top Trading Cycles | answer | 400 |
| 4.4.b | auctions_risk |
Optimal Auction for Bidders with Differing Risk Attitudes | answer | 480 |
| 4.4.c | auctions_affiliated |
Optimal Auction for Bidders with Affiliated Values | answer | 960 |
License
Creative Commons Attribution 4.0 International (CC BY 4.0). You may share and adapt the data with attribution; please cite the paper below.
Citation
@inproceedings{ramansteer,
author = {Narun Krishnamurthi Raman and Taylor Lundy and
Samuel Joseph Amouyal and Yoav Levine and
Kevin Leyton{-}Brown and Moshe Tennenholtz},
title = {{STEER:} {A}ssessing the {E}conomic {R}ationality of
{L}arge {L}anguage {M}odels},
booktitle = {Forty-first International Conference on Machine Learning,
{ICML} 2024, Vienna, Austria, July 21-27, 2024},
publisher = {OpenReview.net},
year = {2024},
url = {https://openreview.net/forum?id=nU1mtFDtMX}
}