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.
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
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. 2) manufacturing industries: LDEMPLit = β0 + β1 LDCONSit + β2 LDPRODit + x0 XXXXXxx + x0 XXXxx + uit (5) and LDEMPLit = β0 + β1 LDCONSit + β2 LDPRODit + x0 XXXXXxx + x0 XXXxx (6) + x0 (XXXxXXXXX)xx + uit where uit = μi + εit and εit ∼iid(0, σ2). We assume the cross-section component μi to be fixed since the 3- digit industries that make-up the panel have not been chosen at random. Hence, both specifications are estimated using a fixed effects estimator that is, basically, OLS with cross-section dummies. The variables used may be defined as follows: LDEMPL = The natural log of the absolute value of the change in employment (L) between t and t-n. LDCONS = The natural log of the absolute value of the change in aparent consumption (C = Q + M - X) between t, t-n, Q being output. LDPROD = The natural log of the absolute value of the change in labour productivity, measured as output per worker, between t and t-n. LTREX = The natural log of trade exposure [(X+M)/Q]. IIT = May be GL, ∆GL or A. IITxLTREX = The interaction between IIT and trade exposure. LDEMPL is a proxy for the costs of adjustment in the labour market. The assumption is that the costs of moving labour across industries is proportional to the size of net changes in wage payments and, furthermore, that this proportion is the same for all industries and over time. The expected sign for the coefficient of LDCONS is positive. One would expect the coefficient of LDPROD to be negative since increases in productivity would tend to reduce the labour requirement to produce the same level of output. This expectation is supported by evidence from the accounting measure of employment change found in, e.g., Xxxxxxxx and Calfat (1999) for Belgium, Xxxxxx et al. (1999) for Greece and Erlat (2000) for Turkey. The prior expectation for the coefficient of LTREX is that it should be positive since trade exposure is expected to increase inter-industry specialization pressures (Brulhart and Xxxxxx, 2000: 730). Finally, the coefficients of both IIT and IITxLTREX are expected to be negative given the smooth adjustment hypothesis. The reason for the introduction of IITxLTREX in the second specification is the expectation that IIT should matter more in sectors where the level of trade is high.
The Model. A A A A A P { } An asynchronous Broadcast protocol (also known as A-cast) [8] or an Asynchronous Byzantine Agree- ment (ABA) [9] protocol is carried out among a set of n parties, say = P1, . . . , Pn , where every two parties are directly connected by a secure channel and t out of the n parties can be under the influence of a computationally unbounded Byzantine (active) adversary, denoted as t. The adversary t, completely dictates the parties under its control and can force them to deviate from a protocol, in any arbitrary manner. The parties not under the influence of t are called honest or uncorrupted. The underlying network is asynchronous, where the communication channels between the parties have arbitrary, yet finite delay (i.e the messages are guaranteed to reach eventually). To model this, t is given the power to schedule the delivery of all messages in the network. However, t can only schedule the messages sent by an honest party, without having any access to them. The inherent difficulty in designing a protocol in asynchronous network, comes from the fact that when a party does not receive an expected message then he cannot decide whether the sender is corrupted (and did not send the message at all) or the message is just delayed in the network. Therefore it is impossible to consider the values sent by all uncorrupted parties. So values of up to t (potentially honest) parties may get ignored because waiting for them could turn out to be endless. Moreover the tools that are applicable in synchronous settings cannot be deployed directly in asynchronous settings. Hence, designing asynchronous protocols require completely new set of primitives. For an excellent introduction to asynchronous protocols, see [9]. We now formally define A-cast and ABA.
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 X ={X0, X
The Model. Ui Uj Ui Uj
The Model. The security of an IB-B-MS scheme is modeled via the following game between a challenger C and an adversary A. Initial: C first runs BM.Setup to obtain a master-secret and the system parameter list params, then sends params to the adversary A while keeping the master-secret secret. A
The Model. 2.1. The Network Model by Goyal and Joshi (2006) Formally, an international agreement between countries i and j is described by a link, given by a binary variable gij {0,1} with gij = 1 if an agreement exists between countries i and j and gij = 0 otherwise. A network gij = {( gij)ijN } is a description of the international agreements that exist among a set N = {1,…,N} of identical countries, where N is the total number of countries. Networks gc and ge are the complete network (i.e. gij = 1 for all i, j N) and the empty network (i.e. gij = 0 for all i, j N). Let G denote the set of all possible networks, g + gij denote the network obtained by replacing gij = 0 in network g by gij = 1, and g − gij denote the network obtained by replacing gij = 1 in network g by gij = 0. Let Ni(g) = {j N: gij = 1} be the set of countries with whom country i has an international trade agreement in network g. Assume that i Ni(g) so that gii = 1. The cardinality of Ni(g) is denoted i(g). In this model i(g) is also the number of active firms in country i because of the assumption that each country has only one firm (note that the domestic firm in country i is included in i(g)). Let Li(g) = {(gij)ijN : j Ni(g)} be the set of links existing in country i in network g. Note that gii Li(g). Let hi Li(g) – {gii} be a link subset, and let i be the cardinality of hi. This latter notation is used in the definition of the alternative stability concept adopted in this research. Let (g) be a subset of countries in network g. (g) is said to be a complete component if: (i) gij = 1 for all i,j (g); and (ii) gik = 0 for all i (g) and all k (g). On the other hand, (g) is said to be an incomplete component if there exists at least two countries i,j (g) such that gij = 0.
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- t
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 i 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 i cost ci(at) to the individual i. The cost is increasing, differentiable and convex n n
