The Model Clause Samples
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The Model. While the historical record shows that the Federal Reserve attempted to use the nonrate terms of access to control discount window borrowing in the 1920s and early 1930s it is not clear the attempts were successful. Assessing whether changes in credit policy had a material effect on bank borrowing requires empirical tests using a model that can measure the administrative pressure applied at the discount window. A useful starting point in developing such a model is in specifying the costs to banks in meeting a reserve need. Cost-minimizing banks would weigh the cost of borrowing from the discount window against the cost of liquidating assets or borrowing from an alternative source. The cost of borrowing from the Federal Reserve is the interest paid at the discount rate, Rd, plus the implicit costs of supervisory surveillance. These costs are those resulting from the administrative pressure supervisors apply to discourage banks from engaging in arbitrage when discount rates were below market rates of interest, as they were for most of the 1920s and many months in the early 1930s. In this model the implicit costs are represented by a borrowing function in which the marginal surveillance cost of access to the discount window, c(B/K), rises with the amount borrowed, B, relative to bank capital, K. Capital is the appropriate scale variable given that Federal Reserve banks had from the start focused on borrowing relative to capital when seeking to restrain bank borrowing (▇▇▇▇▇▇▇▇▇▇▇, 1932, p. 44). The implicit costs may be thought of as the opportunity costs of providing the collateral for the borrowings or the capital adjustments required by supervisors whose attention is drawn to the bank by the borrowing. The cost of liquidating the assets is the foregone interest on the assets, RA, plus the transactions costs incurred in selling the assets, tc, measured as a portion of the value of the assets. Although a small market for federal funds developed in several cities in the 1920s in which banks could borrow or lend reserves, most banks met reserve drains by liquidating assets — particularly their holdings of call loans and short- term marketable securities (▇▇▇▇▇▇, 1938, 93-97 and ▇▇▇▇▇▇, 1964) 3-13). The total cost, C, to banks of meeting their reserve need, RN, can be expressed as: where
(1) C = RdB + c(B/K) + RaA + tcA Rd is the discount rate, c(B/K) is the implicit or surveillance costs involved in borrowing, Ra is the interest rate on the alternative source ...
The Model. 7.1.1 RCC and the MOD will operate within a Public / Public partnership. The overriding objective is to ensure that the site is developed in such a way as to deliver the agreed vision for the site. Hence whatever vehicle is adopted it must ensure that RCC and MOD retain control over exactly what gets delivered.
7.1.2 The MOD will procure a Land Sale Delivery Partner (LSDP) to deliver the project and the development along the same lines as LSDP models used elsewhere for Defence sales
The Model. We consider a team with n members who take part in a joint production repeat- edly. At the beginning of each period, each team member i simultaneously decide whether or not to participate in the production process. Let dt ∈ {0, 1} denote the participation decision for each team member i at each period t. For an individual i who is willing to take part in the production at t, we have dt = 1 and dt = 0 otherwise. For the production to take place at period t, we require that all the team member must involve, that is Q dt = 1.1 At the beginning of each period t, the agents sign a court-enforceable agreement, which specifies: the participation decisions for all agents2 and how the final output will be shared among all the participating agents. Let St denotes the sharing rule3. Then each team member takes an unobservable action at ∈ R+ which incurs a cost ci(at) to the individual i. The cost is increasing, differentiable and convex
The Model. We assume a supply chain consisting of a single retailer (he) and a single supplier (she). The supplier can ship to the retailer in any period but produces only once every T periods. This is appropriate for a setting in which a supplier may manufacture different goods on a set production schedule so that she can only produce material for a given retailer once every T periods. We assume no bound on the amount produced. Alternatively, one could imagine a supplier which, because of limited availability of necessary raw materials, can produce for a given retailer only once every T periods. There is a large body of literature that discusses the benefits of sequencing multiple jobs that must use common resources over a fixed time interval. These types of problems are collectively referred to as economic lot scheduling problems or lot-sizing problems. ▇▇▇▇▇▇▇▇▇▇ (1978) examines both analytical and heuristic techniques for finding policies under no capacity restrictions. ▇▇▇▇▇▇ (1999a) provides additional reasons for using policies with fixed-time-review intervals as well as an excellent review of papers that use this assumption. Other references can be found in ▇▇▇▇▇▇ (1981), which provides a review of production scheduling literature. We further assume that the retailer (under RMI) and the supplier (under VMI) follow a periodic-review inventory policy so that they examine the retailer’s inventory level at the beginning of each period, and that end-user demand occurs only at the retailer. Demand is independently and identically distributed (i.i.d.) according to the distribution function Φ(·) and density function φ(·). Excess demand at the retailer is backlogged. We allow the supplier to outsource in order to obtain material to ship to the retailer in a period in which the supplier cannot produce. In this context outsourcing could represent a form of expediting such as working overtime, producing using less efficient technology, transshipping from another location, or procuring from an outside source. Outsourcing results in an additional cost to the supplier of b0 per unit. When salvage value corresponds to production costs, both salvage value and production costs can be ignored. Therefore, we do not include them in the model. Instead, we model only the premium for outsourced goods, b0. Under both RMI and VMI, as soon as inventory arrives at the retailer, ownership of the inventory is transferred to the retailer. Thus, our situation does not represent a consignmen...
The Model. Formally, a dishonest server S∗ in the SQOM is modeled as follows.
1. S∗ may reliably store the n-qubit state Hc(w)|x⟩ = Hc(w)1 |x1⟩ ⊗ · · · ⊗ Hc(w)n|xn⟩ received in step (1) of NEWQID.
2. At the end of the protocol, in step (5), S∗ chooses an arbitrary sequence θ = (θ1, . . . , θn), where each θi describes an arbitrary orthonormal basis of C2, and measures each qubit Hc(w)i xi in basis θi to observe Yi F2. Hence, we assume that S∗ measures all qubits at the end of the protocol.
3. The choice of θ may depend on all the classical information gathered during the execution of the protocol, but we assume a non-adaptive setting where θi does not depend on Yj for i = j, i.e., S∗ has to choose θ entirely before performing any measurement. Considering complete projective measurements acting on individual qubits, rather than general single-qubit POVMs, may be considered a restriction of our model. Nonetheless, general POVM measurements can always be described by projective measurements on a bigger system. In this sense, restricting to projective mea- surements is consistent with the requirement of single-qubit operations. It seems non-trivial to extend our security proof to general single-qubit POVMs. The restriction to non-adaptive measurements (item 3) is rather strong, even though the protocol from [DFSSo7] already breaks down in this non-adaptive setting. The restriction was introduced as a stepping stone towards proving the adaptive case. Up to now, we have unfortunately not yet succeeded in doing so, hence we leave the adaptive case for future research. We also leave for future research the case of a less restricted dishonest server S∗ that can do measurements on blocks that are less stringently bounded in size. Whereas the adaptive versus non-adaptive issue appears to be a proof-technical problem (NEWQID looks secure also against an adaptive S∗), allowing measurements on larger blocks will require a new protocol, since NEWQID becomes insecure when S∗ can do measurements on blocks of size 2, as we show in Section 5.6.5.
The Model. The security model used to provide proof, models interaction of the real partic- ipants (modeled as oracles) and an adversary via queries which the adversary makes to the oracles. It is a kind of a “game” between the adversary and the participants, where the adversary makes some queries and finally tries to distinguish a group key from a random quantity for some session he chooses. The model is defined in details below: Participants. The set of all potential participants is denoted by ▇ ={▇▇, ▇
The Model. In this section we refine the formal security model which has been widely used in the litera- ture [12, 8–10, 23, 6] to analyze group key agreement protocols. In particular, we incorporate strong corruption [4] into the security model in a different way than the previous approaches by allowing an adversary to ask one additional query, Dump, and we modify the definition of freshness according to the refined model. Section 5 shows that our approach leads to much simpler security proof of the compiler presented by ▇▇▇▇ and Yung [23]. U { } Participants. Let = U1, . . . , Un be a set of n users who wish to participate in a group key agreement protocol P . The number of users, n, is polynomially bounded in the security parameter k. Users may execute the protocol multiple times concurrently and thus each user can have many instances called oracles. We use Πs to denote instance s of user Ui. In initialization phase, each user Ui ∈ U obtains a long-term public/private key pair (PKi, SKi) by running a key generation algorithm G(1k). The set of public keys of all users is assumed to be known a priori to all parties including the adversary A.
The Model. To explore effects of a PTA in services, this section gives a model of a particular service sector that is imperfectly competitive. In addition to the domestic indigenous firms, there are foreign firms who provide services in the home country. There are, however, barriers protecting the domestic firms from competition with foreign firms. Within this framework, we examine the implications of a PTA which eliminates these barriers and promotes the partner country’s firms access to domestic consumers. In this model, there is the home country (1), a partner country (2), and a non-partner country (3). There are ni identical firms in country i ( i ∈{1,2,3}) and they provide a particular service in the home country’s market. The inverse demand for the service in the home country is p = x − y∑niqi , (1) i=1 where p is the market price of the service and qi market by a country i based firm. is the quantity supplied to the Indigenous firms in the home country face a constant marginal cost c, while the foreign firm that is based in country i additionally has to pay ti to provide the service to consumers in the home country.3 This cost may reflect not just cross-border tariffs, but all costs stemming from the restrictions to foreign service providers in the home market. Consequently, marginal costs of each firm are
The Model. We consider a team with n members who take part in a joint production repeat- edly. At the beginning of each period, each team member i simultaneously decide whether or not to participate in the production process. Let dt ∈ {0, 1} denote the participation decision for each team member i at each period t. For an individual i who is willing to take part in the production at t, we have dt = 1 and dt = 0
The Model. As mentioned in the Introduction, we follow Brulhart and ▇▇▇▇▇▇ (2000) and estimate the following two specifications of an equation designed to account for changes in employment in 3-digit ISIC (Rev.