On the distance between cross-impact models: A set of metric measures for cross-impact analysis

This paper attempts to add to the Leibnizian or mathematical side of Technological Forecasting. Given two distinct cross impact models, the paper shows that there exists a set of appropriate metric measures such that the distance between any two cross impact models can be determined. That is, it is possible to say how close or how far apart two or more cross impact models are to one another. It is argued that the ability to make such determinations is vital to the formal and mathematical development and growth of Technological Forecasting.     

Some probability problems associated with cross-impact analysis

Various probability models are discussed which may be applied to Cross-Impact analysis. The bulk of this article is concerned with what is currently the most commonly employed model that utilizes marginal probabilities and either simple conditional probabilities or ”impacts“. Requirements for admissible probability sets are given and some misgivings about current practices are discussed. Bounds are given for the joint occurrence or non-occurrence of a number of events which could replace the Monte Carlo generation currently in use. It is concluded that the multitude of probability problems associated with Cross-Impact analysis are such as to preclude its use as a quantitative technique in its present form although its use as a vehicle for promoting discussion can still be recommended and some of the results can serve as a rough guide in forecasting.     

Scenario construction via Delphi and cross-impact analysis

Since its origins, decision makers have broadly used the Delphi method as a collaborative technique for generating important events and scenarios about what may happen in the future. This is a complex process because of the different interrelations and the potential synergetic effects among the relevant events related to a decision. This fact, along with the uncertainty about the occurrence or non-occurrence of the events, makes the scenario generation task a challenging issue in Delphi processes. In the 1960's, Cross-Impact Analysis (CIA) appeared as a methodological tool for dealing with this complexity. CIA can be used for creating a working model out from a set of significant events. CIA has been combined with other methodological approaches in order to increase its functionality and improve its final outcome. In this paper, the authors propose a new step-by-step model for scenario-analysis based on a merger of Turoff's alternative approach to CIA and the technique called Interpretive Structural Modeling (ISM). The authors' proposal adds tools for detecting critical events and for producing a graphical representation to the previous scenario-generation methods based on CIA. Moreover, it allows working with large sets of events without using large computational infrastructures. The authors present sufficient information and data so that anyone who wishes to may duplicate the implementation of the process. Additionally they make explicit a set of requirements for carrying out a Delphi process for a group to develop a set of significant events, collectively make the estimations of cross impacts, and to support a continuous planning process within an organization. They use two examples to discuss operational issues and practical implications of the model.     

Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend

Does theory aid inflation forecasting? To address this question, we develop a novel forecasting procedure based upon a New Keynesian Phillips Curve that incorporates time-varying trend inflation, to capture shifts in central bank preferences and monetary policy frameworks. We generate theory-implied predictions for both the trend and cyclical components of inflation, and recombine them to obtain an overall inflation forecast. Using quarterly data for the Euro Area and the United States that cover almost half a century, we compare our inflation forecasting procedure against the most popular time series models. We find that our theory-based forecasts outperform these benchmarks that previous studies found difficult to beat. Our results are shown to be robust to structural breaks, geographic areas, and variants of the econometric specification. Our findings suggest that the scepticism concerning the use of theory in forecasting is unwarranted, and theory should continue to play an important role in policymaking.     

Collaborative scenario modeling in emergency management through cross-impact

In this paper we analyze how to apply cross-impact modeling for developing collaborative scenarios in Emergency Preparedness. Scenarios can enhance the understanding of emergency teams about the factors which are involved in the definition of an emergency plan and how different actors participate in it. The version of Cross-Impact Analysis described aims at contributing to this goal through allowing the collaborative development of scenarios out of large event sets. This ultimately reduces the complexity for estimating a working model. In order to illustrate this research effort hypothetical results of a dirty bomb attack scenario exercise are presented, along with the final estimates of relationships based on four rounds of individual estimates followed by discussion of differences in the perceived relationships, in order to achieve a “consensus” model. The purpose of this exercise is to demonstrate the ability of a group to create a working model of the scenario that may be used to examine the consequences of various assumptions about preparedness, plans, and the actions taken during the crisis situation. The method may be used as either a planning tool and/or a training tool. We discuss the process for collecting inputs from a collaborative group and how to improve the consistency of the group inputs in a Delphi-like feedback process. Suggestions for improving details of the wording of items in order to minimize misunderstandings and miscommunication are included, along with suggestions for future extensions to this research.     

A qualitative cross-impact approach to find the key technology

This article presents a qualitative analysis method based on fuzzy relations for a cross-impact model designed for a technology impact assessment. The cross-impact knowledge is often uncertain or fuzzy when dealing with future events. Assessing the cross-impact relationships among future technologies creates a more uncertain or fuzzy situation because of the time and the uncertainty involved in evaluating future technologies. In addition, experts prefer to use linguistic terms or fuzzy values in their predictions. Thus a cross-impact matrix is represented as fuzzy relations on causal concepts. We therefore develop inference algorithms based on fuzzy relations and show a simple technology assessment example to illustrate this approach. This approach is useful in finding the key technology because it considers not only the direct impact but also the indirect impact.     

Cross-impact balances: A system-theoretical approach to cross-impact analysis

Cross-Impact methods are standard tools of the scenario technique. They provide a number of structured processes for the deduction of plausible developments of the future in the form of rough scenarios and are based on expert judgments about systemic interactions. Cross-Impact methods are mostly used for analytical tasks which do not allow the use of theory-based computational models due to their disciplinary heterogeneity and the relevance of “soft” system knowledge, but on the other hand are too complex for a purely argumentative systems analysis. The essentials of a new Cross-Impact approach (Cross-Impact Balance Analysis, CIB) are outlined; it is of high methodological flexibility and is especially suitable for the use in expert discourses due to its transparent analytical logic. Due to its mathematical qualities it is also particularly well suited for the analytical integration of calculable system parts. An application of CIB to a project on the generation of electricity and climate protection is described. For a theoretical foundation of the CIB method relations to systems theory, especially to the theory of dynamic systems, are discussed. This explicates that CIB scenarios correspond to the solutions of slowly time-varying pair-force systems.     

A cross-impact model applicable to forecasts for long-range planning

A methodology is developed, which is based on cross-impact analysis and which is useful in estimating the effects of uncertainty in future sales programs and the effects of program interaction on long-range sales forecasts. The utility of the cross-impact methodology vis-a-vis the conventional decision-tree approach lies in the reduction of complexity made possible when a large number of programs must be considered.     

Time-dependent event cross-impact analysis: Results from a new model

This time-dependent event cross-impact model involves four stages. In the first stage, an undisturbed world view, prior to new information being obtained, is projected for a set of events to occur with specified probability densities over an indefinite time horizon. In the second stage, the estimator fills in the cells of a cross-impact matrix with qualitative estimates of the strength and direction of the impacts. Qualitative cross-impacts are converted to aaquantitative form by the analyst conducting the excercise. In the third stage, it is assumed that new information becomes known to the estimator causing him to project anew the time-dependent probabilities of the events in the set. In the fourth and final stage, an event cross-impact simulation model is constructed in system dynamics and run to produce new probability densities of the event set. These densities take into account both the cross-impacts between events and the effect of the newly obtained information. The possible results include both shifts of the most likely time of occurence and changes in the cumulative probability of the events. The chief usefulness of the model is that it provides a method of foreshortening the time required by decision-makers to take into account the effect of new information on the probability of events occuring over a time period.     

Roadmapping 3G mobile TV: Strategic thinking and scenario planning through repeated cross-impact handling

In order to deal with growing uncertainties emerging in the 3G wireless industry and to preserve their competitiveness, managers involved in the wireless value network should identify future success very early and develop their strategic planning on time. This study, based on a Scenario Evaluation and Analysis through Repeated Cross impact Handling, allows the generation of both qualitative and quantitative scenarios and can be used as an operative planning tool. The dynamic forces driving the scenario are based on the main principles of system thinking and multiple features. The probabilistic data have been elicited with the help of 40 executives in USA and Europe working for companies in the different phases of the wireless value chain. Findings allow to identify basic trends and uncertainties useful to develop corporate or business strategies.     

Scenarios of air transport development to 1990 by SMIC 74—A new cross-impact method

The purpose of this paper is to describe the results of the application of the SMIC 74 method to a study of the air transport development in the Paris area to 1990. SMIC 74 is a new cross-impact method that provides three kinds of results: (a) Consistent information on the events that may occur and influence the evolution of air transport, (b) Cardinal ranking of the possible air transport development scenarios, (c) Sensitivity analysis. This method helps the decision-maker to choose between alternative strategies. For more detailed information the reader should refer to [1–3, 5].     

A method for the analysis of interrelationships between mutually connected events: A cross-impact method

A cross-impact method is developed and adapted to the Socialist planning system, specifically economic growth in Hungary. The method makes it possible to estimate probabilities of occurence of interrelated events, to investigate the chains of effects of such events, and to minimize inconsistencies.     

Innovation forecasting

Technological forecasting is premised on a certain orderliness of the innovation process. Myriad studies of technological substitution, diffusion, and transfer processes have yielded conceptual models of what matters for successful innovation, but most technological forecasts key on limited empirical measures quite divorced from those innovation process models. We glean a number of concepts from various innovation models, then present an array of bibliometric measures that offer the promise of operationalizing these concepts. Judicious combination of such bibliometrics with other forms of evidence offers an enriched form of technological forecasting we call “innovation forecasting.” This provides a good means to combine technological trends, mapping of technological interdependencies, and competitive intelligence to produce a viable forecast. We illustrate by assessing prospects for ceramic engine technologies.     

Deterministic versus stochastic mechanisms in principal-agent models

This paper shows that, contrary to what is generally believed, decreasing concavity of the agent's utility function with respect to the screening variable is not sufficient to ensure that stochastic mechanisms are suboptimal. The paper demonstrates, however, that they are suboptimal whenever the optimal deterministic mechanism exhibits no bunching. This is the case for most applications of the theory and therefore validates the literature's usual focus on deterministic mechanisms.     

Deterministic evolutionary dynamics: A unifying approach

We tackle three components of evolutionary modelling: payoffs, dynamical systems and equilibrium concepts. Firstly, we merely require that fitness functions are continuous. Secondly, we examine very general classes of dynamics. Thirdly, we give useful parallels to the Nash equilibrium and the evolutionarily stable strategy. Under (weakly) sign-compatible dynamics the change in population share of every (at least one) subgroup present in the population corresponds in sign with its relative fitness. At a saturated equilibrium, each subgroup with positive population share has highest fitness. We examine two evolutionary stability concepts: the evolutionarily stable equilibrium and the generalized evolutionarily stable state.     

Continuous forecasting methods

The concept of a continuously operating system of expert opinion, a “collective brain” involving the combination of Delphi and network chart procedures, is discussed. The computer-aided process described by the author uses four types of information: the forecast of external actions of the environment, the values of control parameters as function of time, the current values of internal parameters, and estimates obtained from the principal experts.     

Volatility forecasting: combinations of realized volatility measures and forecasting models

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.