Report of the Parking System Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Parking System Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Ice Arena Facility Revenue Note Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Ice Arena Facility Revenue Note Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Regulated Materials Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Regulated Materials Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Memorial Union Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Memorial Union Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Athletic Facilities Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Athletic Facilities Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Iowa Department of Revenue Performance Plan, FY 2009

Agency Performance Plan, Iowa Department of Revenue

Iowa Workforce Development Revenue Performance Plan, FY 2009

Agency Performance Plan, Iowa Workforce Development

On bounds for network revenue management

The Network Revenue Management problem can be formulated as a stochastic dynamic programming problem (DP or the\optimal" solution V *) whose exact solution is computationally intractable. Consequently, a number of heuristics have been proposed in the literature, the most popular of which are the deterministic linear programming (DLP) model, and a simulation based method, the randomized linear programming (RLP) model. Both methods give upper bounds on the optimal solution value (DLP and PHLP respectively). These bounds are used to provide control values that can be used in practice to make accept/deny decisions for booking requests. Recently Adelman [1] and Topaloglu [18] have proposed alternate upper bounds, the affine relaxation (AR) bound and the Lagrangian relaxation (LR) bound respectively, and showed that their bounds are tighter than the DLP bound. Tight bounds are of great interest as it appears from empirical studies and practical experience that models that give tighter bounds also lead to better controls (better in the sense that they lead to more revenue). In this paper we give tightened versions of three bounds, calling themsAR (strong Affine Relaxation), sLR (strong Lagrangian Relaxation) and sPHLP (strong Perfect Hindsight LP), and show relations between them. Speciffically, we show that the sPHLP bound is tighter than sLR bound and sAR bound is tighter than the LR bound. The techniques for deriving the sLR and sPHLP bounds can potentially be applied to other instances of weakly-coupled dynamic programming.

Iowa Department of Revenue, Strategic Plan, 2007-2010

This report outlines the strategic plan for Iowa Department of Revenue, goals and mission.

Weak and strong altruism in traitgGroups: Reproductive suicide, personal fitness and expected value

A simple variant of trait group selection, employing predators as themechanism underlying group selection, supports contingent reproductivesuicide as altruism (i.e., behavior lowering personal fitness whileaugmenting that of another) without kin assortment. The contingentsuicidal type may either saturate the population or be polymorphicwith a type avoiding suicide, depending on parameters. In addition tocontingent suicide, this randomly assorting morph may also exhibitcontinuously expressed strong altruism (sensu Wilson 1979) usuallythought restricted to kin selection. The model will not, however,support a sterile worker caste as such, where sterility occurs beforelife history events associated with effective altruism; reproductivesuicide must remain fundamentally contingent (facultative sensu WestEberhard 1987; Myles 1988) under random assortment. The continuouslyexpressed strong altruism supported by the model may be reinterpretedas probability of arbitrarily committing reproductive suicide, withoutbenefit for another; such arbitrary suicide (a "load" on "adaptive"suicide) is viable only under a more restricted parameter spacerelative to the necessarily concomitant adaptive contingent suicide.

A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters

Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In this paper, we criticize this model from another angle. Estimating the discrete finite-period model poses problems of indeterminacy and non-robustness: Arbitrarily fixing T leads to arbitrary control values and on the other hand estimating T from data adds an additional layer of indeterminacy. To counter this, we first propose an alternate finite-population model that avoids this problem of fixing T and allows a wider range of demand distributions, while retaining the useful marginal-value properties of the finite-period model. The finite-population model still requires jointly estimating market size and the parameters of the customer purchase model without observing no-purchases. Estimation of market-size when no-purchases are unobservable has rarely been attempted in the marketing or revenue management literature. Indeed, we point out that it is akin to the classical statistical problem of estimating the parameters of a binomial distribution with unknown population size and success probability, and hence likely to be challenging. However, when the purchase probabilities are given by a functional form such as a multinomial-logit model, we propose an estimation heuristic that exploits the specification of the functional form, the variety of the offer sets in a typical RM setting, and qualitative knowledge of arrival rates. Finally we perform simulations to show that the estimator is very promising in obtaining unbiased estimates of population size and the model parameters.

Annual Report of the Iowa Department of Revenue FY2008

Annual Report of the Iowa Department of Revenue FY2008

Addendum to Annual Report of the Iowa Department of Revenue FY2008

Addendum to Annual Report of the Iowa Department of Revenue FY2008

Iowa Department of Revenue Performance Report, FY06

The Iowa Department of Revenue Performance Report is presented in accordance with the Accountable Government Act to improve decision-making and increase accountability to stakeholders and citizens of Iowa.