Game Theory Models
Utilized to understand and model strategic auction behavior, each bidder has an unspecified demand function for the item in question. Certain models, which are based on private valuations, relate to instances where there is private information (such as private estimates on intellectual property), while other models are based upon public information (for example, when companies are bidding on rights to a revenue stream.) In particular, the marginal bidding decision is based upon the expected private bidder surplus, which is the difference between the valuation and the bid price. Often times, bidders end up overpaying for an item based upon the winner’s curse, a condition where competitive bidding leads to suboptimal outcomes in the context of competition.

Generalized Second-Price Auctions
A type of sealed-bid auctions, Vickrey auctions define a broad class of auctions where the winner pays the second-highest price for the good. Most commonly, this structure is used in Internet auctions where the winner bidder pays the second highest bid plus a marginal amount over that bid, as determined through proxy bids. Another form of generalized 2nd price auction were the FCC’s wireless spectrum auctions to allocate spectrum to telecommunications companies for cellular and data communications.

Ascending, English Auctions
The “traditional” auction format is based upon an opening suggested bid, followed by visible bids, which gradually increase the price until the winning bidder is solidified. These auctions are common in the art and collectibles world, although variants have emerged to allow for proxy bids by global bidders. In order to estimate and forecast English Auction outcomes, analysts must create an estimated value for the item, as many collectible auction houses do.

The basic problem facing the field revolves around how to value various care options, as well as how to structure pro-health incentives outside of care; policy makers must structure an insurance system that provides for broad coverage without creating moral hazard, or conditions which might lead to sub-optimal consumer behavior. As a result, the problems facing health care economists are quite difficult, especially in the context of the various interest groups in the public policy sector. Modern health care outcome models are based upon consumers as both implicit producers, and, indirect consumers, of health. In this sense, health is a type of human capital, which can be augmented based upon care, education and proper decisions, parallel to the role that education plays.

Where health analysts commonly disagree is how to find the “optimal” level of health care – while early models suggested the optimal health investment occurs where marginal benefit equals marginal costs, measuring these variables (objectively) has proved to be difficult. Additionally, the public-private nature of the industry has made it difficult for policy makers to separate ethical and normative considerations from more objective measures. One of the most difficult questions is how to pool risk without denying the benefits of private coverage – the costs of treating uninsured patients has increasingly fallen upon government and private institutions, which are forced to pass on these costs to “healthy” patients. By pooling large groups of individuals together, policy makers are creating models that reduce overall risk and incentivize preventative care.

The Coase Theorem
Formulated by economist Ronald Coase, the Theorem poses an efficient solution of property rights in the context of externalities. At a basic level, the solution states that costs can be incorporated into decisions between private parties in the absence of regulation or government regulation. The original problem came about when Coase analyzed property rights in the context of radio frequency interference – while the FCC sought a solution by seeking to change the structure of the FM and AM allocations, Coase proposed in his paper “The Problem of Social Cost” that stations should be allowed to freely buy and sell frequency levels, allowing them to buy up blocks of frequencies close to theirs to eliminate complications. This solution has been, in turn, applied to a variety of legal problems related to externalities, especially in environmental law, and led to Coase being awarded the Nobel Prize for Economics. In practice, the Theorem has greatly influenced tort law, when Judge Hand began applying cost-benefit analysis to property dispute cases. Many scholars have argued that transactions costs and regulations are too costly to allow the private system to work, especially in cases of indirect pollution and unseen costs.

Kaldor Hicks Efficiency
A central concept in economic efficiency related to Pareto efficiency conditions, Kaldor-Hicks seeks to identify conditions in which utility of various parties might be improved through alternate allocation. In a standard sense, allocations are considered Pareto-efficient where the utility of at least one party is improved, while no others are harmed – on the other hand, Kaldor-Hicks allows for the “winning” parties to monetarily compensate the losers in order to clear a transaction. The theory has wide-ranging implications for property and environmental law, in addition to contribution broad solutions to management problems involving conflicting party interests.

Optimal Control Models
As the basis for variational calculus optimization, optimal control emerged from engineering and found a variety of applications in economic planning. In particular, the system defines a series of differential equations that model the path of costs according to changes in variables. By starting from an initial condition (state), the model seeks to understand how to maximize a function limited by a series of constraints – for example, engineers may want to evaluate the best possible fuel economy of a given design based upon road conditions and optimal control can help suggest improvements in controls to help improve these outcomes. In general, problems in the field are non-linear and often solved by way of numerical methods, which require computing solutions such as MATLAB to find a numerical solution.

Convex Optimization
As the basis for optimization on classical economic problems, convex optimization seeks to answer the question of finding the maximum or minimum values within a function. As a result, a variety of problems, from theoretical utility maximization to least-squares regression analysis, rely upon convex techniques to find solutions. A common problem within the field is to minimize a function (such as costs) subject to constraints (such as output requirements or labor costs) to determine optimal business allocation, especially when managers are allocating resources across various locations.

Bioinformatics
A new field within molecular biology, bioinformatics applies databases to help solve modeling problems in biology. Among the most innovative techniques within the field is the development of large scale databases to develop accurate models of protein structures. As a result, the field has led to innovations in data mining and machine learning techniques which have been ported over to business analysis.

Computational Genomics
In the racing to model the human genome, Craig Venter led a private research team which utilized computing power to out-paced government-funded efforts. Since that time, leading scientists have been working on techniques to improve the speed of genetic analysis, which has greatly accelerated many efforts in pharmaceutical research.

Molecular Modeling
A broad field that has helped to provide a better understanding of the behavior of molecular compounds for improvements in material science and chemical research, molecular modeling has been used to help scientists better understand protein folding and enzyme behavior. The field has, therefore, been central in helping to design new, improved materials and drugs, as well as leading to a number of software programs which are now also used in social sciences such as Gaussian.

The model formed the basis for innovations in financial theory, which were recognized with a Nobel Prize awarded to economists Harry Markowitz and Merton Miller. In particular, the model allows for the expected returns on a class of assets to be the base interest rate (essentially, the rate on government bonds – the risk-free rate) plus the risk premium factors (scaled by beta, or the movement of the asset relative to the larger market.) Over the past few years, many forecasters created models in which the risk factors were under-stated based upon the belief that returns on investments would continue to grow (especially in real estate) while the overall health of the market would remain steady. In fact, these forecasts were created in order to justify securitization of assets, by passing them on to 3rd party investors. Modern portfolio theory, in practice, established an equilibrium of returns relative to risk; any anomalies in return will be adjusted based on improved information – where risk is greater, prices of assets will fall. One of the bases of portfolio theory is optimization – financial analysts seek to find the “efficient frontier”, which is the highest level of return for a given level of risk allowance – diversifying assets across several classes and markets, while adequately accounting for risk is the direction that financial models are beginning to return to.

Behavioral analysts go beyond traditional regression analysis to seek out variables which are difficult to measure, such as heuristics (the “rules of thumb” which form cognitive biases), as well as market inefficiencies (such as “bubbles” and other non-rational forms of behavior.) While traditional models of consumer decision making, attributed to pricing, competition and income constraints can explain many purchasing decisions, researchers have found that intangibles, including habits and recommendations, often have a large impact upon consumer decisions.

In order to incorporate these factors, economists have moved beyond basic marginal analysis to understand decision making through prospect theory: developed by Nobel Laureates Kahneman and Tversky, Prospect Theory analyzes consumer behavior through the lens of bounded rationality – consumer make decisions using their existing information (reference points) relative to their expected goals. Data analysts often critique traditional economic models for assuming perfect foresight and information on the part of consumers; the theory, on the other hand, allows for an explanation of biases such as risk aversion. As a result, when firms set prices or make decisions on their market offerings, it is common for product managers to review larger (qualitative) market trends, which are increasingly being incorporated into data models. The future of consumer theory is creating data sets from behavior that has traditionally been difficult to measure.

The package we tested is STATA MP, which allows for over 10,000 independent variables across any number of observations; while we ran the PC version, both Mac and Linux users can run the software natively as well. For smaller data sets, the IC edition provides a budget version which is limited to just over 2,000 independent variables (a trial “Small” version provides up to 99 variables across 1,000 observations.) While the software can be a bit expensive – close to $1,000 for a single-user license – I found the built-in commands and options to save a lot of time relative to less structured packages.

Within minutes, you can load data in from a variety of sources (XML, CSV or virtually any database) and begin performing commands, with integrated options for regression, survey sampling models and, of course, summary statistics. Version 10 has much improved graphing capabilities, allowing you to produce clean, concise reports for research or professional papers. STATA provides on-going support to their users by way of on-line manuals, technical support engineers and training seminars – all of which make it a good community to join as an analyst.

Ensuring that different relational databases can interaction requires a standard set of API (application programming interface) standards that allow database administrators to centralize data for management. API formats including ODBC and OLE have innovated an entirely new way to access and aggregate different sources of data. Open Database Connectivity (ODBC) is the leading API standard, which is based upon SQL queries and is widely used since it is platform-independent. The goal of these APIs is to provide a single data stream that can be distributed throughout the various tools that firms use to make decisions, from Customer Relationship (CRM) software to back-end dashboards.

As companies continue to invest in data integration technology, a large majority of these budgets are being allocated to Integration, which aims to (ultimately) reduce IT costs by combining data sources. With the movement to network-based systems, many corporate IT departments have been prioritizing investments in database software over new forms of hardware. The larger shift of technology budgets towards software is helping to foster innovation both in database and virtual server technology; in the context of a recession, many companies are looking to vastly reduce hardware costs and save on licensing fees from multiple database vendors.

Pentaho Open Source Intelligence
As one of the most complete Open Source intelligence applications, Pentaho has been adopted by a number of leading corporations including Oracle, Sun Microsystems and IBM. Founded in 2004 by former IBM executives, the software has evolved to become a multi-platform java-based software package to provide a complete suite of offerings ranging from OLAP analysis to data mining and dashboard creation.

IBM Cognos
Independent for decades, Cognos formally became a subsidiary of IBM last year and has grown to one of the most widely adopted business intelligence packages with industry-specific solutions. In the 8th generation of its software release, nearly every company can find a custom solution tailored to unique KPIs (key performance indicators.)

SAP Business Objects
Founded in Paris, SAP Business Objects has expanded to over 40,000 world wide customers and now operates from Silicon Valley as a part of SAP after its 2007 acquisition. The third generation Business Objects software features specialized reporting features that bring together various corporate data sources to produce “Crystal” reports, known for their clear interpretations of data.