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The game tree alternates MAX nodes for joint robot choices and CHANCE nodes for joint monster outcomes. An interior MAX node has at most $25$ children; an interior CHANCE node at most $4^2=16$. A terminal safe meeting has utility one and capture has utility zero. With no specified preference for speed, the objective is to maximize the probability of eventual safe meeting.

Use expectimax: maximize at MAX nodes and take the expected value at CHANCE nodes. For nonterminal states, its finite-horizon backup isHere the horizon counts half-turns, terminal values stay fixed at zero or one, and $V_0$ is zero at nonterminal states. This computes the best chance of success within the horizon. Maximizing the worst monster outcome would solve a different problem from the stipulated random movement.

Because waiting and movement cycles can repeat states, the infinite game tree need not terminate. For the unbounded objective, use value iteration on the finite state graph, starting from terminal-success indicators as above: $V_t$ increases to the optimal eventual-success probability. A reachability-policy method or sufficiently long finite-horizon policies can extract the actions; arbitrary tie choices in the limiting equations must not select a policy that waits forever instead of realizing the value. Alternatively impose an explicit finite horizon and use depth-limited expectimax, stating that changed objective. No method guarantees success with probability one from every initial configuration.

The complete market makes every state-contingent consumption vector attainable. Its budget is ${\sum }_s{p}_s{c}_s=100$, and expected logarithmic utility under the equally likely states is $\frac{1}{4}{\sum }_s\log c_s$, with $c_s>0$. Increasing utility makes the budget bind. For a Lagrange multiplier $\eta$, the first-order conditions areSubstitution in the budget gives $1/\eta =100$, so $\eta =0.01$ andEvery state receives an expenditure $p_s{c}_s=25$, rather than equal consumption. Strict concavity of logarithmic utility makes this feasible stationary point the unique global maximizer. The physical probabilities enter the utility objective; the supporting prices enter the budget, and they must not be interchanged.

The current price of one unit of certain future consumption is the sum of the Arrow state prices, namely $0.9$. A unit-price risk-free asset yielding gross return $R$ must satisfy $0.9R=1$. HenceNormalize the Arrow state prices by their sum, or multiply them by $R$, to obtain the risk-neutral probabilities:They are positive and sum to one. Any terminal payoff $Y$ has current price ${\sum }_s{p}_s{Y}_s=R^{-1}{\mathbb{E}}_{Q}Y$. In particular any asset with current price one has ${\mathbb{E}}_Q{r}_i=R$. These pricing probabilities need not equal the physical probabilities governing the investor's utility.

${\ell }_2\succ {\ell }_1$. The distinction is small but strict: their normalized expected utilities are $18/25$ and $7/10$, respectively.

By the Von Neumann-Morgenstern utility theorem, use a representing utility function and apply a positive affine function to set $u({\omega }_1)=1$ and $u({\omega }_4)=0$. This is possible because the given strict preference implies $u({\omega }_1)>u({\omega }_4)$. The two specified indifference relations then giveTaking the expected utility of each lottery yieldsTheir difference is $1/50>0$, establishing the stated strict preference. These numbers measure utility, not monetary reward or an assumed ordering of the subscripts.

Here is the underlying argument directly in terms of the axioms. The independence axiom for lottery preferences permits substitution of indifferent lotteries inside a common mixture. Replacing ${\omega }_3$ by its given mixture of ${\omega }_1,{\omega }_4$, and then replacing ${\omega }_2$, followed by reduction of compound lotteries, givesand consequentlyThe transitive relation axiom combines the successive substitutions. Finally, mixture monotonicity says that among mixtures of a strictly better and a strictly worse outcome, the greater probability of the better outcome is strictly preferred; this follows from the independence axiom for lottery preferences and transitivity. Since $18/25>7/10$ and ${\omega }_1\succ {\omega }_4$, the comparison follows. Complete lottery preferences and continuity of lottery preferences are part of the representation theorem's hypotheses; the direct substitution calculation uses the already supplied indifferences and does not need to invoke continuity of lottery preferences again.^[ref]

Yes: the stated order has an expected utility representation. For outcomes whose monetary amounts are $1,2,4$, respectively, chooseThenSince $12/5>9/5>3/2>4/5$, this utility function gives exactly the required strict preferences.

The distinction is that the expected utility hypothesis does not by itself require the utility function to increase with monetary amount. This nonmonotone monetary utility ranks the outcome paying 2 below the outcome paying 1. If increasing monetary utility were imposed as an additional assumption, the stated order would fail: ${\ell }_3\succ {\ell }_4$ requires $u(1)>u(2)$ becauseThat additional monotonicity assumption is not printed in this part.

Assign distinct rooms along a hallway to participants. A participant's utility is their own room value plus a positive bonus if their designated friend receives a physically adjacent room; participants without friends receive only their room value. When friendships form disjoint pairs, consecutive choosing slots allow each pair to settle its own adjacency before later participants act. Consecutive slots in serial dictatorship do not themselves require adjacent allocated rooms.

An allocation procedure using a fixed priority order: each participant in turn selects one remaining object, which is permanently assigned to them. For participants whose utilities depend only on their own assigned objects, strict rankings give a unique best available choice and the resulting allocation is Pareto optimal. If a proposed Pareto improvement changes an assignment, its earliest changed participant loses their unique best available object. With ties or allocation externalities, that argument requires additional assumptions.

Consider a logistic choice rule $P(\mathrm{accept})=1/(1+\exp (-U))$ with $U=\alpha R-\kappa E$ and fixed noise scale. At one positive offer $(R_0,E_0)$, changes satisfying $\delta \alpha R_0=\delta \kappa E_0$ leave utility and acceptance probability unchanged. One offer therefore cannot identify reward gain and effort cost separately.

For example, $(\alpha ,\kappa )=(0.6,0.2)$ and $(1,1)$ both give $U=1$ at $(R,E)=(2,1)$. Their predictions differ at other offers. Independently varying reward and effort can identify the two coefficients within this model when the design has rank two and choice data are informative. If noise scale is also unknown, its tradeoff with the coefficients requires additional constraints.

Utility represents how a decision maker ranks outcomes. A utility function assigns numbers to alternatives consistently with those preference relations; ordinal comparisons depend on the ordering rather than the particular numerical scale.

In expected-utility portfolio choice, an increasing utility function prefers greater wealth, while concavity models aversion to risk.

A utility function assigns numerical values to outcomes consistently with preference comparisons. It represents utility; the representation and the preference concept are distinct.