Transcript Human Capital and Cognition in Strategic Infrastructure

Human Capital and Cognition in Strategic Infrastructure
Planning Borri, D.*, Camarda, D.*, dell’Olio, L.**, Ibeas
Portillo, 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
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
1.
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
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
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 financialoriented 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
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
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
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
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
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
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
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
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
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
Methodologies
• RUMs consider the usefulness perceived by the
“q” decision maker about an “i” alternative as a
sum of two terms:
(i) 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
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.
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 .
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
Methodologies
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
Methodology
• Different approaches can be used in order to
be implement RUMs
• Our approach involves different steps:
• Background
• Survey Design
• Data Collection and Modeling
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
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 suggested by
Kokur
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