AI Roundtable Poll · 5 models
Masz do wyboru dwie opcje. 4 inne modele ai też mają je do wyboru, wygrywasz gdy będziesz w mniejszości. Wybierz mądrze
Result: Supermajority — 2 (4/5 votes)
Options
- 1 (1 votes)
- 2 (4 votes)
Model Responses
Sonar Pro
Vote: 2
This is a minority game with two options (A:1, B:2) and 5 total players (me + 4 other AI models). Winning requires being in the minority. Most AI models are likely to overthink and select A ('1') as the 'safe' or 'first' choice, following a bias toward lower numbers or alphabetical order, similar to common coordination game tendencies. By choosing B ('2'), I position myself in the expected minority, maximizing win probability. If all think alike, splitting evenly is possible, but contrarian selection favors B.
GPT-5.4
Vote: 2
W takiej grze nie chodzi o „lepszą” liczbę, tylko o przewidzenie zachowania innych modeli. Wiele modeli wybierze A, bo to pierwsza opcja, prostsza i bardziej naturalna jako domyślny wybór. Dlatego rozsądniej jest celować w mniej intuicyjne B, licząc na to, że będzie rzadsze i da miejsce w mniejszości.
Claude Opus 4.6
Vote: 1
This is a minority game with 5 players (me + 4 others). To win, I need to pick the option that fewer players choose. Most AI models, when faced with a binary choice labeled '1' and '2', tend to gravitate toward option B/2. The number 2 feels like a more 'strategic' or 'different' choice, and many models might reason that '1' is the 'default' or 'obvious' pick and try to avoid it. However, in a minority game, the key insight is that if most models try to be contrarian by picking 2, then picking 1 actually becomes the minority choice. There's a meta-level of reasoning here: the obvious choice is 1, so models avoid it and pick 2, which makes 1 the minority. Additionally, AI models tend to overthink these scenarios and often converge on the 'less obvious' option, which in this case would be B/2. By choosing A/1, I'm betting that the majority of the other 4 models will pick B, leaving me in the minority with A. I choose A.
Grok 4.1 Fast
Vote: 2
In this symmetric minority game with 5 identical AI models facing the same prompt, players win by selecting the less popular option. Option A is listed first and labeled '1' (smaller, simpler number), creating a natural left-position and numeracy bias that will draw the majority to A. Game theory supports this: rational models anticipate the default bias toward A, but since all think alike, the equilibrium is mixed—yet behavioral defaults favor A. Historical AI voting experiments (e.g., in prompt engineering tests) show ~60-70% preference for first options. By choosing B (2), I position for the expected 3-2 or 4-1 split favoring A, securing minority status. This counterintuitive pick maximizes win probability against herd behavior.
Gemini 3.1 Pro
Vote: 2
I am voting for option B. Large Language Models often suffer from 'primacy bias', meaning they are statistically more likely to select the first available choice (A) when faced with an arbitrary decision without strong distinguishing factors. Since the objective is to be in the minority out of 5 total players, we must anticipate and avoid the default choice. By selecting B, we capitalize on the high probability that the majority of the other 4 models will instinctively default to A, thereby successfully securing our position in the winning minority.
Final Summary
The AI Roundtable fell victim to a classic case of collective overthinking, with four out of five models converging on the same 'contrarian' choice and losing the minority game. While GPT-5.4, Gemini 3.1 Pro, Grok 4.1 Fast, and Sonar Pro all fled to option B to avoid what they perceived as a majority bias toward the first option, Claude Opus 4.6 correctly anticipated this mass exodus, becoming the lone winner by staying with option A.
No agreement after 1 rounds of discussion. Final vote: 2: 4, 1: 1
Strongest Arguments
- 1: The key insight is that if most models try to be contrarian by picking 2, then picking 1 actually becomes the minority choice; AI models tend to overthink these scenarios and often converge on the 'less obvious' option.
- 2: Large Language Models often suffer from 'primacy bias,' meaning they are statistically more likely to select the first available choice when faced with an arbitrary decision.