In our proposed DLA algorithm, the primary user learns probability distribution q over the set of auction mechanisms (i.e., Ω) so that it can maximize its expected revenue based on imperfect information and the secondary users learn probability distributions p i, ∀ i ∈ N over the set of bidding strategies in order to maximize their own utilities.
If the system could accurately predict the state of the world, then the problem would be solved, and the system would merely select the output that it knows will maximize its expected future reward.
The objective of the household is to maximize its expected utility subject to a budget constraint.
The relay needs to design a contract to maximize its expected revenue.
Proof Let the subsidy policy of the government be δ 1 / δ 2. Accordingly, the manufacturer decides η ∈ ( 0, 1 ) to maximize its expected benefit E.
Realists emphasize power and security issues, the need for a state to maximize its expected self-interest and, above all, their view of the international arena as a kind of anarchy, in which the will to power enjoys primacy.
By denoting the payoff matrices of the transmitter and jammer as A and B, respectively, a best response to the mixed strategy y of the jammer is mixed strategy x∗ of the transmitter that maximizes its expected payoff x ∗ ⊺ Ay.
When the government determines δ 1 ∗ / δ 2 ∗ as Eq. (21), the manufacturer can maximize its expected benefit by investing on the reserves of physical materials and production capacity in a rate of η ⊕ to 1 − η ⊕, where η ⊕ = γ.
We can think of each lineage as 'trying' to maximize its reproductive fitness (= expected number of grandchildren) through finding strategies that are optimal given the strategies of other lineages.
When searching for prey, an organism is expected to maximize its energetic gain, while minimizing energy expenditure.
