This paper delves into the transformative potential of Applied Information Economics (AIE) in assessing IT investments. Understand how to quantify uncertainty, evaluate intangible benefits, and make financially meaningful, strategic IT decisions.
<span class="Normal">This paper evaluates The Balanced Scorecard (BSC), Applied Information Economics (AIE) and Post Implementation Review (PIR) – for some inexplicable reason leaving out the other proud members of the framework alphabet soup – as IT Investment Management frameworks. The evaluation is done using Analytic Hierarchy Process (AHP). </span>
This paper presents the results of a case study undertaken by the Federal Chief Information Officers Council to ascertain the applicability of Applied Information Economics (AIE) and the Balanced Scorecard in measuring IT value.
This report presents the results of Applied Information Economics (AIE) in action in a real life setting – Environmental Protection Agency (EPA) wanted to ascertain the implications of replacing desktops on an accelerated schedule. EPA used AIE to create a business case for this "investment". Here is the report from that study.
Which is better; Applied Information Economics (AIE) or Balanced Scorecard (BSC)? Read On…
Enclosed is an introduction to "Applied Information Economics" aka AIE. The author – Douglas Hubbard – provides the value proposition for the value proposition measuring framework! Good Read!
Applied Information Economics (AIE) is a method for quantifying the value of IT investments. This overview of AIE provides an overview of key IT decision problem, how AIE solves them and how AIE is different from other methods.
Applied Information Economics (AIE) is a decision analysis method that combines elements of decision theory, risk analysis, and Monte Carlo methods to help organizations make better decisions. It focuses on improving the accuracy of probability assessments, computing the value of additional information, and using empirical methods in a selective and focused manner to reduce uncertainty. The AIE method also includes optimization techniques such as modern portfolio theory to determine the best risk and return positions for a set of alternatives. The ultimate goal of AIE is to help organizations effectively allocate resources by measuring and managing information that affects their outcomes.
Applied Information Economics (AIE) utilizes the Monte Carlo method and other proven techniques from decision theory and risk analysis. Unlike traditional cost-benefit analysis and accounting-style business cases, AIE does not rely solely on point estimates of uncertain values and instead models uncertainty explicitly.
AIE begins by “calibrating” estimators to ensure that the probabilities assigned are neither overconfident nor underconfident. The value of information calculation is used to guide further measurement efforts, and all values are converted into economic terms to allow for financial and economic optimization methods to be employed.
One of the key benefits of AIE is that it provides a disciplined quantification of the variability in financial projections and the information necessary to reduce that variability. It is more elaborate than other methods, but the extra effort is deemed necessary for larger, riskier decisions.
However, the AIE methodology is complex and requires an analytical background to understand and implement, which may limit its adoption by managers accustomed to traditional cost-benefit analysis. Some of the same limitations as Monte Carlo simulations apply, such as the possibility of excluding important factors or variables being covariant.
Additionally, there is little research in academic literature to show the long-term benefits of AIE. However, it is based on previously-developed components that have a sound theoretical basis and empirical evidence of improving on unaided intuition or other popular decision analysis methods.
This chapter provides information and resources on AIE for CIOs.
