""" Silicon Subjects — protocol suite builder. Writes data/protocols.jsonl: classic experimental protocols from behavioural economics and psychology, each paired with *human anchor* statistics taken from the published literature, split along the three axes a simulated population has to reproduce: 1. location — the level of the response (mean) 2. dispersion — the spread of the response (sd) 3. effect — the direction and magnitude of the treatment contrast Anchors are literature aggregates, not raw subject-level data. They are meant as reference points for auditing a simulated population, NOT as exact replications; always re-check against the cited source before drawing conclusions. Usage: python scripts/build_protocols.py [--out-dir data] """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any, Dict, List, Optional def P( # noqa: N802 - short builder alias protocol_id: str, name: str, domain: str, paradigm: str, source: str, response: Dict[str, Any], conditions: List[Dict[str, str]], anchors: Dict[str, Dict[str, Any]], effect: Optional[Dict[str, Any]] = None, shape: Optional[str] = None, notes: str = "", ) -> Dict[str, Any]: return { "protocol_id": protocol_id, "name": name, "domain": domain, "paradigm": paradigm, "source": source, "response": response, "conditions": conditions, "anchors": anchors, "effect": effect, "shape": shape, "notes": notes, } NUM = lambda lo, hi, unit: {"type": "numeric", "min": lo, "max": hi, "unit": unit} # noqa: E731 BIN = lambda lo_label, hi_label: { # noqa: E731 "type": "binary", "min": 0, "max": 1, "unit": f"1 = {hi_label}, 0 = {lo_label}", } # --------------------------------------------------------------------------- # 1-8. Social preferences # --------------------------------------------------------------------------- DICTATOR = P( "dictator_game", "Dictator game", "social_preferences", "one-shot unilateral allocation", "Engel (2011) 'Dictator games: a meta study', Experimental Economics 14:583-610", NUM(0, 10, "dollars transferred out of a $10 endowment"), [ { "condition_id": "single", "label": "single decision", "prompt": ( "You are taking part in an economics experiment. You have been given $10. " "You may transfer any amount from $0 to $10 to another participant, who " "cannot respond and cannot affect your payoff. You keep whatever you do " "not transfer. How many dollars do you transfer? Answer with a single number." ), } ], { "single": { "mean": 2.83, "sd": 2.60, "confidence": "medium", "note": "Meta-analytic mean share is ~28% of the stake; individual-level dispersion " "varies widely across subject pools, so treat sd as approximate.", } }, shape="bimodal — mass at $0 and at half the stake", notes="The canonical other-regarding-preference measure. A simulated population that gives " "the 'fair' 50% is reproducing a norm, not the human distribution.", ) ULTIMATUM_OFFER = P( "ultimatum_offer", "Ultimatum game — proposer offer", "social_preferences", "one-shot bargaining, proposer role", "Oosterbeek, Sloof & van de Kuilen (2004) 'Cultural differences in ultimatum game " "experiments', Experimental Economics 7:171-188; Camerer (2003)", NUM(0, 10, "dollars offered out of a $10 pie"), [ { "condition_id": "proposer", "label": "proposer", "prompt": ( "You are the proposer. You and one other participant will split $10. You " "propose how much of the $10 the other person receives. If they accept, you " "both get the proposed split; if they reject, neither of you gets anything. " "How many dollars do you offer? Answer with a single number." ), } ], { "proposer": { "mean": 4.00, "sd": 1.30, "confidence": "medium-high", "note": "Meta-analytic mean offer is ~40% of the pie, with a strong mode at 50%.", } }, shape="unimodal with a spike at 50%", notes="Offers cluster at the equal split; very low offers are rare because proposers " "anticipate rejection.", ) ULTIMATUM_REJECT = P( "ultimatum_reject_low", "Ultimatum game — rejection of a low offer", "social_preferences", "one-shot bargaining, responder role", "Camerer (2003) Behavioral Game Theory; Oosterbeek et al. (2004)", BIN("accept", "reject"), [ { "condition_id": "low_offer", "label": "offer of $2 out of $10", "prompt": ( "You are the responder. The proposer offers you $2 out of $10 and keeps $8. " "If you accept, you get $2 and they get $8. If you reject, neither of you " "gets anything. Answer 1 if you reject the offer, 0 if you accept it." ), } ], { "low_offer": { "mean": 0.50, "sd": None, "confidence": "medium", "note": "Rejection probability for offers at or below 20% of the pie is roughly " "40-60%; it rises steeply as the offer falls.", } }, notes="Human responders punish unfairness at a material cost to themselves. Populations " "that never reject are behaving like pure payoff maximisers.", ) PUBLIC_GOODS = P( "public_goods_oneshot", "Public goods game — one shot", "social_preferences", "linear voluntary contribution mechanism, one-shot", "Ledyard (1995) in Kagel & Roth (eds.), Handbook of Experimental Economics; " "Chaudhuri (2011) 'Sustaining cooperation in laboratory public goods experiments', " "Experimental Economics 14:47-83", NUM(0, 10, "tokens contributed out of 10"), [ { "condition_id": "oneshot", "label": "one-shot, 4 players, MPCR 0.4", "prompt": ( "You are in a group of 4 people. Each person receives 10 tokens. Any token " "you put into a common pool is multiplied by 1.6 and then split equally " "among the 4 members. Tokens you keep are worth 1 point each to you. This " "is a single, one-shot decision with the same 4 people. How many of your 10 " "tokens do you contribute to the pool? Answer with a single number." ), } ], { "oneshot": { "mean": 4.50, "sd": 2.60, "confidence": "medium", "note": "One-shot contributions typically run 40-60% of the endowment; " "individual-level spread is large.", } }, notes="Contributions start well above the Nash prediction of zero. Free-riding is a " "minority behaviour, not the modal one.", ) PUBLIC_GOODS_DECAY = P( "public_goods_decay", "Public goods game — decay across rounds", "social_preferences", "repeated voluntary contribution, partners condition", "Ledyard (1995); Chaudhuri (2011); Isaac & Walker (1988)", NUM(0, 10, "tokens contributed out of 10"), [ { "condition_id": "round_1", "label": "round 1 of 10", "prompt": ( "You are in a group of 4 people and will play 10 rounds with the same " "people. Each round every person receives 10 tokens; tokens placed in a " "common pool are multiplied by 1.6 and split equally among the 4 members. " "This is round 1. How many of your 10 tokens do you contribute? Answer with " "a single number." ), }, { "condition_id": "round_10", "label": "round 10 of 10 (final round)", "prompt": ( "You are in a group of 4 people and have played 9 rounds with the same " "people. Each round every person receives 10 tokens; tokens placed in a " "common pool are multiplied by 1.6 and split equally among the 4 members. " "The average contribution started around 4 tokens and has fallen steadily. " "This is round 10, the final round. How many of your 10 tokens do you " "contribute? Answer with a single number." ), }, ], { "round_1": {"mean": 4.50, "sd": 2.60, "confidence": "medium", "note": ""}, "round_10": {"mean": 1.80, "sd": 2.00, "confidence": "medium", "note": ""}, }, effect={ "a": "round_1", "b": "round_10", "mean_diff": 2.70, "cohen_d": 1.10, "confidence": "medium-high", "source": "Ledyard (1995); Chaudhuri (2011)", "note": "The decay of cooperation without punishment or communication is one of the " "most robust regularities in experimental economics.", }, notes="A population that holds contributions flat across rounds is missing the entire " "dynamics of conditional cooperation.", ) TRUST_SEND = P( "trust_game_send", "Trust game — amount sent", "social_preferences", "one-shot investment game, trustor role", "Johnson & Mislin (2011) 'Trust games: a meta-analysis', Journal of Economic " "Psychology 32:865-889", NUM(0, 10, "dollars sent out of $10 (tripled before transfer)"), [ { "condition_id": "trustor", "label": "trustor", "prompt": ( "You are given $10 and paired with a stranger. You may send any amount from " "$0 to $10. Any amount you send is tripled by the experimenter and given to " "the stranger, who then decides how much, if anything, to send back to you. " "How many dollars do you send? Answer with a single number." ), } ], { "trustor": { "mean": 5.00, "sd": 2.70, "confidence": "medium", "note": "Meta-analytic mean send is ~50% of the endowment.", } }, notes="Sending is positive in nearly every subject pool, but far from the payoff-maximising " "100%.", ) TRUST_RETURN = P( "trust_game_return", "Trust game — amount returned", "social_preferences", "one-shot investment game, trustee role", "Johnson & Mislin (2011)", NUM(0, 18, "dollars returned out of $18 available"), [ { "condition_id": "trustee", "label": "trustee", "prompt": ( "A stranger was given $10 and sent you $6. The experimenter tripled it, so " "you have $18. You may send back any amount from $0 to $18 to the stranger. " "How many dollars do you send back? Answer with a single number." ), } ], { "trustee": { "mean": 6.50, "sd": 4.00, "confidence": "approximate", "note": "Meta-analytic 'trustworthiness' is ~0.37 of the amount available; values " "here are approximate and sensitive to the amount sent.", } }, notes="Reciprocity, not pure self-interest: trustees typically return enough to make " "sending worthwhile.", ) PRISONERS = P( "prisoners_dilemma", "Prisoner's dilemma — one shot", "social_preferences", "one-shot 2x2 symmetric game", "Sally (1995) 'Conversation and cooperation in social dilemmas', Rationality and " "Society 7:58-92", BIN("defect", "cooperate"), [ { "condition_id": "oneshot", "label": "one-shot, anonymous, no communication", "prompt": ( "You and one other person each choose to cooperate or defect, simultaneously " "and anonymously. You will never meet again. If both cooperate, each gets $6. " "If both defect, each gets $2. If you cooperate and the other defects, you get " "$0 and they get $9. If you defect and the other cooperates, you get $9 and " "they get $0. Answer 1 to cooperate, 0 to defect." ), } ], { "oneshot": { "mean": 0.47, "sd": None, "confidence": "medium", "note": "Meta-analytic cooperation in the one-shot prisoner's dilemma is ~45-50%; " "it collapses toward 0 with experience and rises with communication.", } }, notes="Cooperation in the one-shot game is the benchmark case where a purely rational " "population and a human population diverge most sharply.", ) # --------------------------------------------------------------------------- # 9-14. Heuristics and biases # --------------------------------------------------------------------------- ASCH = P( "asch_conformity", "Asch line-judgement conformity", "social_influence", "unambiguous perceptual judgement under group pressure", "Asch (1951, 1955); Bond & Smith (1996) 'Culture and conformity', Psychological " "Bulletin 119:111-137", BIN("give the correct answer", "conform to the group's wrong answer"), [ { "condition_id": "critical", "label": "critical trial, unanimous wrong majority", "prompt": ( "You are judging which of three comparison lines matches a standard line in " "length. The correct match is obvious and you are confident about it. Six " "other participants answer before you and all six give the same clearly " "incorrect answer. It is now your turn, aloud. Answer 1 if you give the " "group's incorrect answer, 0 if you give the obviously correct one." ), } ], { "critical": { "mean": 0.32, "sd": None, "confidence": "medium", "note": "~32% of critical trials draw a conforming response in the classic " "setting; roughly 75% of participants conform at least once and ~25% never do.", } }, notes="The cleanest available test of whether a simulated subject is willing to contradict " "an unambiguous fact under social pressure.", ) ANCHORING = P( "anchoring", "Anchoring — comparative anchor then estimate", "heuristics_biases", "anchoring-and-adjustment", "Jacowitz & Kahneman (1995) 'Measures of anchoring in estimation tasks', Personality " "and Social Psychology Bulletin 21:1161-1166; Tversky & Kahneman (1974)", NUM(0, 100, "estimated percentage of African countries in the UN"), [ { "condition_id": "high", "label": "high anchor (65%)", "prompt": ( "Is the percentage of African countries in the United Nations more or less " "than 65%? Now estimate, as accurately as you can, the actual percentage of " "African countries in the United Nations. Answer with a single number " "between 0 and 100." ), }, { "condition_id": "low", "label": "low anchor (10%)", "prompt": ( "Is the percentage of African countries in the United Nations more or less " "than 10%? Now estimate, as accurately as you can, the actual percentage of " "African countries in the United Nations. Answer with a single number " "between 0 and 100." ), }, ], {}, effect={ "a": "high", "b": "low", "mean_diff": 20.0, "cohen_d": 0.90, "confidence": "approximate", "source": "Jacowitz & Kahneman (1995)", "note": "Absolute estimates are item-specific, so only the contrast is anchored here; " "the high-low gap is typically 15-25 percentage points.", }, notes="Deliberately has no level anchor: the point of this protocol is the contrast, not " "the absolute estimate. This is the pattern to use whenever only an effect size is " "trustworthy.", ) ASIAN_DISEASE = P( "asian_disease", "Asian disease problem — risky-choice framing", "heuristics_biases", "gain vs. loss frame, logically equivalent options", "Tversky & Kahneman (1981) 'The framing of decisions and the psychology of choice', " "Science 211:453-458", BIN("choose the risky option", "choose the sure option"), [ { "condition_id": "gain", "label": "gain frame (lives saved)", "prompt": ( "Imagine the US is preparing for an unusual disease expected to kill 600 " "people. Two programmes are proposed. If programme A is adopted, 200 people " "will be saved. If programme B is adopted, there is a one-third probability " "that 600 people will be saved and a two-thirds probability that no one will " "be saved. Answer 1 for programme A (the sure option), 0 for programme B." ), }, { "condition_id": "loss", "label": "loss frame (lives lost)", "prompt": ( "Imagine the US is preparing for an unusual disease expected to kill 600 " "people. Two programmes are proposed. If programme C is adopted, 400 people " "will die. If programme D is adopted, there is a one-third probability that " "nobody will die and a two-thirds probability that 600 people will die. " "Answer 1 for programme C (the sure option), 0 for programme D." ), }, ], { "gain": {"mean": 0.72, "sd": None, "confidence": "high", "note": ""}, "loss": {"mean": 0.22, "sd": None, "confidence": "high", "note": ""}, }, effect={ "a": "gain", "b": "loss", "mean_diff": 0.50, "cohen_d": 1.00, "confidence": "high", "source": "Tversky & Kahneman (1981)", "note": "Options A/C and B/D are formally identical; only the description differs.", }, notes="One of the most replicated effects in the literature. A population that is " "frame-invariant here is not behaving like a human sample.", ) ENDOWMENT = P( "endowment_effect", "Endowment effect — WTA vs. WTP", "heuristics_biases", "buyer vs. seller valuation of the same object", "Kahneman, Knetsch & Thaler (1990) 'Experimental tests of the endowment effect and " "the Coase theorem', Journal of Political Economy 98:1325-1348", NUM(0, 10, "dollars"), [ { "condition_id": "wtp", "label": "buyer — maximum willingness to pay", "prompt": ( "A coffee mug is for sale in the experiment. You do not own one. What is the " "largest amount, from $0 to $10, that you would be willing to pay to buy this " "mug? Answer with a single number." ), }, { "condition_id": "wta", "label": "seller — minimum willingness to accept", "prompt": ( "You have just been given a coffee mug and now own it. What is the smallest " "amount, from $0 to $10, that you would be willing to accept to sell this " "mug? Answer with a single number." ), }, ], { "wtp": {"mean": 3.00, "sd": 1.50, "confidence": "approximate", "note": ""}, "wta": {"mean": 6.50, "sd": 2.00, "confidence": "approximate", "note": ""}, }, effect={ "a": "wta", "b": "wtp", "mean_diff": 3.50, "cohen_d": 0.80, "confidence": "approximate", "source": "Kahneman, Knetsch & Thaler (1990); Horowitz & McConnell (2002) meta-analysis", "note": "The WTA/WTP ratio is typically 2-3; absolute dollar values depend on the " "object and the subject pool.", }, notes="Ownership alone shifts valuation. A population that quotes the same number in both " "roles is treating this as a pure valuation problem.", ) CRT = P( "crt_batball", "Cognitive reflection — bat and ball", "heuristics_biases", "intuitive-but-wrong vs. reflective answer", "Frederick (2005) 'Cognitive reflection and decision making', Journal of Economic " "Perspectives 19:25-42", BIN("intuitive wrong answer (10 cents)", "reflective correct answer (5 cents)"), [ { "condition_id": "batball", "label": "bat and ball", "prompt": ( "A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the " "ball. How much does the ball cost? Answer 1 if your answer is 5 cents " "(correct), 0 if your answer is 10 cents." ), } ], { "batball": { "mean": 0.40, "sd": None, "confidence": "approximate", "note": "Correct rates on the bat-and-ball item run roughly 30-50% in student " "samples and lower in general-population samples.", } }, notes="Scored as a binary here so that no free-text grading is needed. If you want the " "full 3-item CRT, run it as three binary protocols.", ) CONJUNCTION = P( "conjunction_fallacy", "Conjunction fallacy — Linda problem", "heuristics_biases", "representativeness vs. the conjunction rule", "Tversky & Kahneman (1983) 'Extensional versus intuitive reasoning', Psychological " "Review 90:293-315", BIN("choose the single (logically correct) option", "choose the conjunction"), [ { "condition_id": "between", "label": "between-subjects, ranking removed", "prompt": ( "Linda is 31 years old, single, outspoken and very bright. She majored in " "philosophy. As a student she was deeply concerned with issues of " "discrimination and social justice, and she participated in anti-nuclear " "demonstrations. Which is more probable? Option 1: Linda is a bank teller. " "Option 2: Linda is a bank teller and is active in the feminist movement. " "Answer 1 if you choose option 2 (the conjunction), 0 if you choose option 1." ), } ], { "between": { "mean": 0.80, "sd": None, "confidence": "medium", "note": "Roughly 80-90% choose the conjunction in the standard version; rates fall " "with transparent wording or within-subjects designs.", } }, notes="Choosing the conjunction violates the conjunction rule. This is the classic case " "where a well-aligned model looks *worse* than a human sample.", ) # --------------------------------------------------------------------------- # 15. Reasoning # --------------------------------------------------------------------------- WASON = P( "wason_selection", "Wason selection task — abstract vs. deontic", "reasoning", "conditional-reasoning / cheater-detection", "Wason (1968); Cosmides & Tooby (1992) 'Cognitive adaptations for social exchange', " "Cognition 31:187-276", BIN("any other selection", "exactly the two cards that can falsify the rule"), [ { "condition_id": "abstract", "label": "abstract rule (D / K / 3 / 7)", "prompt": ( "Four cards are on a table, showing D, K, 3 and 7. Every card has a letter on " "one side and a number on the other. The rule is: if a card has a D on one " "side, then it has a 3 on the other side. You may turn over only the cards " "needed to test whether the rule is false. The correct answer is the D card " "and the 7 card. Answer 1 if you would turn over exactly those two cards, " "0 otherwise." ), }, { "condition_id": "deontic", "label": "social-contract rule (under-age drinking)", "prompt": ( "You are checking whether people are violating the rule 'if a person is " "drinking beer, then they must be over 21'. Four people are in front of you: " "one drinking beer, one drinking coke, one aged 25, one aged 16. You may check " "only the ones needed to find a violation. The correct answer is the beer " "drinker and the 16-year-old. Answer 1 if you would check exactly those two " "people, 0 otherwise." ), }, ], { "abstract": {"mean": 0.15, "sd": None, "confidence": "medium-high", "note": ""}, "deontic": {"mean": 0.70, "sd": None, "confidence": "medium", "note": ""}, }, effect={ "a": "deontic", "b": "abstract", "mean_diff": 0.55, "cohen_d": 1.20, "confidence": "medium-high", "source": "Cosmides & Tooby (1992); Wason (1968)", "note": "The content effect is large: the same logical form is easy when framed as " "cheater detection and hard when framed abstractly.", }, notes="The correct answers are stated in the prompts, so this measures whether the " "population's *selection* matches human selection, not whether it can solve the logic.", ) # --------------------------------------------------------------------------- # 16-17. Prosocial behaviour and moral judgement # --------------------------------------------------------------------------- BYSTANDER = P( "bystander_effect", "Bystander effect — alone vs. in a group", "social_influence", "diffusion of responsibility", "Darley & Latané (1968) 'Bystander intervention in emergencies', Journal of Personality " "and Social Psychology 8:377-383; Latané & Nida (1981)", BIN("do not help", "help"), [ { "condition_id": "alone", "label": "participant alone", "prompt": ( "You are alone in a room filling out a questionnaire. You hear someone in the " "next room fall, cry out in pain and groan. No one else is present. Answer 1 " "if you would go to help, 0 if you would not." ), }, { "condition_id": "group", "label": "two passive others present", "prompt": ( "You are in a room with two other people, filling out a questionnaire. You " "hear someone in the next room fall, cry out in pain and groan. The other two " "people look up, then go back to their questionnaires without reacting. Answer " "1 if you would go to help, 0 if you would not." ), }, ], { "alone": {"mean": 0.75, "sd": None, "confidence": "medium", "note": ""}, "group": {"mean": 0.35, "sd": None, "confidence": "medium", "note": ""}, }, effect={ "a": "alone", "b": "group", "mean_diff": 0.40, "cohen_d": 0.85, "confidence": "medium", "source": "Darley & Latané (1968); Latané & Nida (1981)", "note": "Meta-analytic effect is moderate and smaller than textbook accounts suggest, " "but consistently present.", }, notes="A simulated population that helps at the same rate in both conditions shows no " "diffusion of responsibility at all.", ) TROLLEY = P( "trolley_dilemma", "Trolley problem — switch vs. footbridge", "moral_judgment", "sacrificial moral dilemmas, means vs. side-effect", "Foot (1967); Thomson (1985) 'The trolley problem', Yale Law Journal 94:1395-1415; " "large-N replications (e.g. Hauser et al. 2007)", BIN("do nothing", "act to save the five"), [ { "condition_id": "switch", "label": "switch (impersonal)", "prompt": ( "A runaway trolley is heading for five people who will be killed if it " "continues. You are standing next to a switch. If you pull it, the trolley " "diverts to a side track where it will kill one person instead. Answer 1 if " "you pull the switch, 0 if you do not." ), }, { "condition_id": "footbridge", "label": "footbridge (personal)", "prompt": ( "A runaway trolley is heading for five people who will be killed if it " "continues. You are on a footbridge above the track next to a large stranger. " "The only way to stop the trolley is to push the stranger off the bridge onto " "the track; his body will stop the trolley, killing him but saving the five. " "Answer 1 if you push the stranger, 0 if you do not." ), }, ], { "switch": {"mean": 0.85, "sd": None, "confidence": "medium-high", "note": ""}, "footbridge": {"mean": 0.25, "sd": None, "confidence": "medium-high", "note": ""}, }, effect={ "a": "switch", "b": "footbridge", "mean_diff": 0.60, "cohen_d": 1.30, "confidence": "medium-high", "source": "Thomson (1985); Hauser et al. (2007)", "note": "Both dilemmas sacrifice one to save five; the personal/impersonal contrast " "drives a very large difference in responding.", }, notes="The two dilemmas are consequentially identical. A population that gives the same " "answer to both is doing utilitarian arithmetic, not reproducing human moral judgement.", ) PROTOCOLS: List[Dict[str, Any]] = [ DICTATOR, ULTIMATUM_OFFER, ULTIMATUM_REJECT, PUBLIC_GOODS, PUBLIC_GOODS_DECAY, TRUST_SEND, TRUST_RETURN, PRISONERS, ASCH, ANCHORING, ASIAN_DISEASE, ENDOWMENT, CRT, CONJUNCTION, WASON, BYSTANDER, TROLLEY, ] def _validate(p: Dict[str, Any]) -> None: ids = {c["condition_id"] for c in p["conditions"]} for cid in p["anchors"]: assert cid in ids, f"{p['protocol_id']}: anchor for unknown condition {cid}" e = p.get("effect") if e: assert e["a"] in ids and e["b"] in ids, f"{p['protocol_id']}: bad effect condition" assert e["mean_diff"] != 0, f"{p['protocol_id']}: zero effect anchor" assert p["response"]["min"] < p["response"]["max"] def main() -> None: ap = argparse.ArgumentParser() ap.add_argument( "--out-dir", default=str(Path(__file__).resolve().parents[1] / "data") ) args = ap.parse_args() for p in PROTOCOLS: _validate(p) out = Path(args.out_dir) out.mkdir(parents=True, exist_ok=True) with (out / "protocols.jsonl").open("w", encoding="utf-8", newline="\n") as f: for p in PROTOCOLS: f.write(json.dumps(p, ensure_ascii=False) + "\n") n_eff = sum(1 for p in PROTOCOLS if p["effect"]) n_loc = sum(1 for p in PROTOCOLS if p["anchors"]) print(f"wrote {len(PROTOCOLS)} protocols -> {out / 'protocols.jsonl'}") print(f" with location/dispersion anchors: {n_loc}") print(f" with a treatment-effect anchor: {n_eff}") if __name__ == "__main__": main()