XV SIET Conference Transport, Spatial Organization and Sustainable Economic Development

1. Human Capital and Cognition in StrategicInfrastructure Planning Borri, D.*, Camarda, D.*, dell’Olio, L.**, IbeasPortillo, A.**, Lovreglio, R.*** * Department of Civil and Environmental Engineering and of Chemistry, Technical University of Bari, Italy, ** Department of Transport and Logistics, University of Cantabria, Santander, Spain, *** Department of Transportation, Napier University, Edinburgh, Scotland XV SIET Conference Transport, Spatial Organization and Sustainable Economic Development Venice September 18-20, 2013

2. DECISION PROCESSES UNDER RISK AND UNCERTAINTY Rational model of spatial planning, in the early 1950s, evaluation as ancillary aspect of planning Increasing criticisms have then followed rational planning models, basically pushed by a quest for democracy and more realism toward the limitation of basic information and knowledge Therefore, the multi-stage rational model was abandoned, toward more fluid and less deterministic planning processes 1.

3. DECISION PROCESSES UNDER RISK AND UNCERTAINTY • As a consequence, increasingly structural embodiment of evaluation and decision in the planning process • In particular, attention toward strategic evaluation of planning phases has emerged, particularly connected with the need of achieving shared, aware and environmentally sustainable objectives • Decision making in risky and uncertain situations as important element in such evaluation streamline in planning processes

4. DECISION PROCESSES UNDER RISK AND UNCERTAINTY • Initial approaches to spatial planning occurred in contexts of safe mobility and of risky sanitarian conditions • In the 1970s and 1980s, spatial temporal reasoning started influencing risk analysis and decision making in risky and uncertain environments • Yet, spatial planners are more interested in macro-risks, such as natural or economic risks: risky economic contexts have been extensively dealt with, often using a financial-oriented focus, but more recently focusing on macroeconomic risk analysis, especially under the push of equitable development issues • On the natural risk side, vulnerability and extreme events have been largely discussed in literature

5. DECISION PROCESSES UNDER RISK AND UNCERTAINTY • Complexity in socio-natural environments involves dramatic increase in uncertainty • Traditional deterministic and quantitative approach to planning and decision making in risky and uncertain contexts increasingly seems to fall short in environmental domains • Research on evaluation and decision approaches under uncertain spatial conditions of risk is today still preliminary and largely context-taylored: nteresting discussions and results emerge for example from studies in the military sector, concerned with selecting alternative walking paths in unstructured risky environments

6. DECISION PROCESSES UNDER RISK AND UNCERTAINTY • But real situations, even in rigidly organized contexts, involve decisions that are characterized by high levels of uncertainty and black box conditions • An increasingly acknowledged approach is the quest for solutions to partially known problems through the so-called naive physics: the basic idea is to avoid exploring risk problems and contexts thoroughly, aiming at setting up and investigating on reality representations that are partial but cognitively and operationally manageable

7. DECISION PROCESSES UNDER RISK AND UNCERTAINTY • Our work is just oriented to embed uncertainty and uncertain environments in risk-incumbent decision processes, so escaping the traditional reticence to a complex-systems approach and focusing on the critical role of agents’ cognitions and behaviours in knowledge elicitation

8. AGENT-BASED DECISIONMAKING • Two design approaches can be used to evaluate safety levels in structures and infrastructures in risky situations: (i) prescription-based, (ii) performance-based • Performance Based Design (PBD): more flexible than the more prescriptive traditional one; allows designers can cope with several different scenarios of risky situations; complex use more because of the need of predictive models predictive which are able to reproduce reality • Evaluation of satisfying conditions of human use of infrastructures in risky situations: complex because evaluation models must incorporate a number of factors which are influenced by human behavior and thus by psycho-social features

9. AGENT-BASED DECISIONMAKING • Recent literature suggest splitting the large number of the factors which influence human behavior in coping with use of infrastructures in risky and uncertain situations, in internal factors and external factors • Both experimental models and reality of risky situations have showed that one of the most important external factors is the social interaction between the people involved in the process • In fact, the agents’ decisions to adopt new, less dangerous, infrastructures are strongly conditioned by choices and behaviors of other agents and decision makers • Many authors have also shown that decision making is influenced by environmental conditions

10. AGENT-BASED DECISIONMAKING • The ‘internal’ factors which characterize human behavior can be divided into physical and cognitive factors: emotions, cultural background, previous experiences, level of familiarity of the decision maker with the environment

11. AGENT-BASED DECISIONMAKING • The need to explain and simulate human behavior has led to a number of behavioral theories and the creation of a number of models for engineering design of environmentally safe infrastructure

12. AGENT-BASED DECISIONMAKING • Important contribution in this modeling process for transport infrastructure from Random Utility Models (RUMs): these models allow uncertainty of human behaviors can be embedded into one or more random components

13. Methodologies • Choices taken by decision makers when they are put in front of a finite set of alternatives can be modeled through the use of the DCMs (Dynamic Causal Models) and particularly the RUMs • Thurstone (1927) introduced RUMs in scientific literature

14. Methodologies • RUMs consider the usefulness perceived by the “q” decision maker about an “i” alternative as a sum of two terms: • the first one is a systematic quantity that estimates the main or expected value of the perceived utility (ii) the second one, called random residual, calculates deviation of the average utility from the real value, embedding all factors that make the decision-making model deviate from pure rationality

15. Methodologies • Scientific literature provides various models of random utility • We use Mixed Logit Models (MLMs) because of their flexibility: they are based on the hypothesis that random residuals are independent and identically distributed according to a Gumbel random distribution with a mean equal to zero and with a λ parameter • In fact, any random utility model can be approximated by MLMs overcoming the limitations of standard Logit models • The decision maker’s varying tastes can also be modeled by the use of random distributions for the coefficients θik.

16. Methodologies • Thus, the probability of choosing an alternative is given by the following equation • …………… • where Pjq has the expression of the well known probability from Multinomial Logit Models • while ………. • is the vector of the generic values that are assumed by the θik coefficients and have probability • and, finally, • ……… • is the vector of parameters characterizing the probability distribution .

17. Methodologies • To date, RUMs have been used in different economical fields (such as marketing, finance, etc.) but have also been by different authors to solve transportation issues • Deterministic approaches are not able to take advantage of the differences and uncertainties inherent in the choice process: RUMs can embed these uncertainties and then provide results that best suit the real processes of choice

18. Methodologies

19. Methodologies • Another great advantage shown by RUMs: implementation of formulations that depend not only on the variables characterizing the environment but also on the characteristics of the decision-makers themselves and the variability of their tastes • However, the more complex RUMs (i.e. Mixed Logit) can present integral expressions requiring numerical solutions that increase the computational burden

20. Methodology • Different approaches can be used in order to be implement RUMs • Our approach involves different steps: • Background • Survey Design • Data Collection and Modeling

21. Methodology • The first area embeds all the steps needed to the definition of the problem in order to find all the variables that characterize it: • Focus Groups (FGs) are needed in order to avoid missing any of the variables implicated in the problem • FGs are generally complex and may require their own methodological framework

22. Methodology • The second area involves the development of the survey • Our surveys on transport infrastructures are divided in two different parts: - in the first part a demographic survey is used in order to collect all the information characterizing the interviewee (all this information is useful during the modeling phase because utility functions could also depend on the intrinsic characteristics of the decision maker) - in the second part different scenarios (which are designed according to the variables characterizing the issue) are implemented: an orthogonal scenario design is determined through the use of a tabular method which is suggestedby Kokur

23. Methodology • The last area involves all the steps needed to determinate the optimal model based on the answers given by the interviewees using statistical criteria