physics

Subjective expected relative similarity

Subjective expected relative similarity is a physics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Subjective expected relative similarity rather than just read about it. In short: Subjective expected relative similarity (SERS) is a normative and descriptive theory that predicts and explains cooperation levels in a family of games termed Similarity Sensitive Games (SSG), among them the well-known Prisoner's Dilemma game (PD). SERS was originally developed in order to (i) provide a new rational solution to the PD game and (ii) to predict human behavior in single-step PD games.

Key takeaways

Subjective expected relative similarity belongs to physics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Subjective expected relative similarity to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Subjective expected relative similarity from memory before moving on to harder problems.

Reference excerpt

Subjective expected relative similarity (SERS) is a normative and descriptive theory that predicts and explains cooperation levels in a family of games termed Similarity Sensitive Games (SSG), among them the well-known Prisoner's Dilemma game (PD). SERS was originally developed in order to (i) provide a new rational solution to the PD game and (ii) to predict human behavior in single-step PD games. It was further developed to account for: (i) repeated PD games, (ii) evolutionary perspectives and, as mentioned above, (iii) the SSG subgroup of 2×2 games. SERS predicts that individuals cooperate whenever their subjectively perceived similarity with their opponent exceeds a situational index derived from the game's payoffs, termed the similarity threshold of the game. SERS proposes a solution to the rational paradox associated with the single step PD and provides accurate behavioral predictions. The theory was developed by Prof. Ilan Fischer at the University of Haifa.

The Prisoner's Dilemma

The dilemma is described by a 2 × 2 payoff matrix that allows each player to choose between a cooperative and a competitive (or defective) move. If both players cooperate, each player obtains the reward (R) payoff. If both defect, each player obtains the punishment (P) payoff. However, if one player defects while the other cooperates, the defector obtains the temptation (T) payoff and the cooperator obtains the sucker's (S) payoff, where T > R > P > S {\displaystyle T>R>P>S} (and, R ≥ T + S 2 {\textstyle R\geq {\frac {T+S}{2}}} assuring that sharing the payoffs awarded for uncoordinated choices does not exceed the payoffs obtained by mutual cooperation). Given the payoff structure of the game (see Table 1), each individual player has a dominant strategy of defection. This dominant strategy yields a better payoff regardless of the opponent's choice. By choosing to defect, players protect themselves from exploitation and retain the option to exploit a trusting opponent. Because this is the case for both players, mutual defection is the only Nash equilibrium of the game. However, this is a deficient equilibrium (since mutual cooperation results in a better payoff for both players). The PD game payoff matrix:

The repeated prisoner's dilemma

Players that knowingly interact for several games (where the end point of the game is unknown), thus playing a repeated Prisoner's Dilemma game, may still be motivated to cooperate with their opponent while attempting to maximise their payoffs along the entire set of their repeated games. Such players face a different challenge of choosing an efficient and lucrative strategy for the repeated play. This challenge may become more complex when individuals are embedded in an ecology, having to face many opponents with various and unknown strategies.

… excerpt ends here. Continue reading the full article.

Illustrations

Subjective expected relative similarity: Fig. 2: The transition of MaRS among three mimicry modes: enacted, expected, and excluded. Each transition is conditioned upon the detection of up-to-date similarity levels by the passive similarity index psp, the reactive similarity index psr, or both. The criterion for sufficient similarity is the similarity threshold ps* defined by SERS — Fig. 2: The transition of MaRS among three mimicry modes: enacted, expected, and excluded. Each transition is conditioned upon the detection of up-to-date similarity levels by the passive similarity index psp, the reactive similarity index psr, or both. The criterion for sufficient similarity is the similarity threshold ps* defined by SERS

Worked examples

Example 1 — a first encounter with Subjective expected relative similarity

Start with the simplest possible case. Write down what Subjective expected relative similarity claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Subjective expected relative similarity before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Subjective expected relative similarity ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Subjective expected relative similarity

In research: Subjective expected relative similarity appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Subjective expected relative similarity in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Subjective expected relative similarity is common in secondary-school and first-year university syllabi. It links to neighbouring topics Game theory, so understanding it makes those chapters shorter.
In everyday life: Look for Subjective expected relative similarity outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Subjective expected relative similarity” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Subjective expected relative similarity in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Subjective expected relative similarity means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Subjective expected relative similarity out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Subjective expected relative similarity in simple terms?

Why does Subjective expected relative similarity matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Subjective expected relative similarity?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Subjective expected relative similarity.