The Model Sample Clauses

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
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The Model. As mentioned in the Introduction, we follow Brulhart and Xxxxxx (2000) and estimate the following two specifications of an equation designed to account for changes in employment in 3-digit ISIC (Rev.
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 (Xxxxxxxxxxx, 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 (Xxxxxx, 1938, 93-97 and Xxxxxx, 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. 2.1. The Network Model by Goyal and Joshi (2006)
The Model. 2.1. The basic game (1) This hypothesis may be seen as representing synergistic e↵ects in the target implemen- tation and/or convexity of individual costs. As for the second interpretation, think of m firms that have to share equally an aggregate abatement level. If their individual xxxxx- ment costs are increasing in individual abatement, then clearly individual participation costs c(m) are decreasing. If, in addition, individual abatement costs are convex, it is easily shown that total abatement costs mc(m) are decreasing6. Moreover, the regulator may consider the achievement of a given target by too few agents is cost-inefficient or not feasible. It is thus assumed that if some participation threshold w > 1 is not reached, the VA fails and a mandatory regulation is enforced instead. As a collective threat, the mandatory regulation typically applies to each of the n firms, whatever their individual willingness to undertake voluntary action may be. Conversely, if the threshold is reached, the VA succeeds and only the m w participating firms implement the global target, each of them bearing the cost c(m). It follows firm i’s payo↵s are defined by: > u(si, m)= 8>< —c(m) if si = 1, m ≤ w 0 if si = 0, m ≤ w —t if m< w, (2) where t is the cost to comply to the mandatory regulation. I also assume that a VA is cost-e↵ective, meaning that if at least w firms participate, they prefer, as a whole, the VA to the mandatory regulation: t> c(w). This condition is one of profitability in the sense 5Such a cap may be for instance to conform some given share of a sector output with an efficiency standard (e.g. the ACEA agreement), or to reduce targeted agents’ toxically releases to some limit value (e.g. the EPA’s 33/50 Program).‌ 6If the aggregate abatement level is A, and individual abatement cost function is d(.), then total costs are md (A/m), and this function is decreasing when d(.) is convex. of self-enforcing equilibrium,7 and it has for direct consequence that the Xxxx solution is non trivial. Aside from profitability, the participation threshold and the threat parameter are exogenous in this model, so as to keep the analysis as general as possible. However, their setting may be constrained if considered within some given institutional context8, as illustrated in section 6. I now turn to the presentation of the correlating device.
The Model. A A A A A P { }
The Model. Consider a population of size one of patients with a specific disease. Patients are indexed with a parameter θ that represents their personal characteristics such as age, co-morbidities, or even some analytical parameter (cholesterol level, blood pressure, biomarker, etc.). We assume that θ is distributed uni- formly within the interval [0, 1]. A pharmaceutical firm has developed a new drug whose therapeutic value has been previously proved in a clinical trial. A clinical trial is defined by {θt , q(θt )} where θt ∈(0,1) represents the characteristics of the patients above which the new drug is tested and q(θ t )∈ (0,1] is the probability that the drug is effective (that is to say, the drug cures, meaning in this setting that it restores completely the quality of life pre- vious to the disease). In other words, patients with θ ≥ θt participate in the clinical trial, and they are cured with probability q(θt ). For the sake of simplicity, we will assume that the probability that the drug cures in the clinical trial is one: q(θt ) = 1. For patients with θ < θt , the clinical trial does not provide any information about the drug efficacy. In real clinical practice, the drug can be administered to patients with θ < θt but its effectiveness is uncertain. We assume that Pr (cure θ < θ ) = θ . Thus, the drug cures with a low probability if it is ad-
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The Model. Consider a two-period world with a zero discount rate and three dates: date -1, date 0, and date 1. There are two units, A and B. Each unit's utility is a linear function of its resources at date 1 (so that there are no wealth e ects).
The Model. Each County is allotted a maximum annual performance incentive amount of $30,770, except for Collin County which is allotted a maximum annual performance amount of $50,770. Performance incentives amounts are calculated quarterly based on the percent of critical* errors of all site/structure addressing points (SSAPs) in the 9-1-1 Addressing Authority’s area of responsibility. *Critical errors are defined as errors that cause, or have a potential of causing, a critical fault in the routing of an 9-1-1 emergency service request call to the correct PSAP. The following GIS features are considered “critical”: • Duplicate SSAP (Site Structure Address Point) • SSAP No Value (no attribution in feature) • Road Centerline (RCL) Range Overlaps • RCL No Value (no attribution in feature) • Boundary Topology Overlaps (Emergency Service Boundaries and jurisdictional boundaries) • Boundary Topology Gaps (Emergency service boundaries and jurisdictional boundaries) There are five performance tiers that allow for different levels of performance equating to different amounts of incentive the 9-1-1 Addressing Authority will receive for that quarter. A formula is used to determine the “workload” of Addressing Authorities and is defined as the total number of critical errors divided by the total number of Site Structure Address Points. The outcome of the formula places the Addressing Authority in the respective tier. Performance incentive amounts are calculated each quarter using the following method: Tier 1 = (# of critical errors / # SSAPs) ≤ .2% or .002 – Receive full annual incentive amount 2 NCT9-1-1 routes landline and VoIP calls using geospatial data. Future standards require all calls, including wireless, to use geospatial data to route emergency calls. Tier 2 = (# of critical errors / # SSAPs) ≤ .4% or .004 – Receive 90% of annual incentive amount Tier 3 = (# of critical errors / # SSAPs) ≤ .6% or .006 – Receive 80% of annual incentive amount Tier 4 = (# of critical errors / # SSAPs) ≤ .8% or .008 – Receive 70% of annual incentive amount Tier 5 = (# of critical errors / # SSAPs) > .8% or .008 – Receive no incentive amount The aggregate of the incentive is divided by four to equate to a quarterly distribution.
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.
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