Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007TASK COMPLEXITY IN MULTI-ATTRIBUTE STATED CHOICE
ENVIRONMENTAL VALUATION
Michelle PARUMOG
Research fellow
Institute for Transport Policy Studies
3-18-19 Toranomon, Minato-ku, Tokyo
105-0001 Japan
Fax: +81-3-5470-8419
E-mail: parumog@jterc.or.jp
Ryuji KAKIMOTO
Associate Professor
Department of Civil and Environmental
Engineering, Kumamoto University
2-39-1 Kurokami, Kumamoto City
860-8555 Japan
Fax: 81-96-342-3507
Email: kakimoto@gpo.kumamoto-u.ac.jp

Shoshi MIZOKAMI
Professor
Department of Civil and Environmental
Engineering, Kumamoto University
2-39-1 Kurokami, Kumamoto City
860-8555 Japan
Fax: +81-96-342-3507
Email: smizo@gpo.kumamoto-u.ac.jp

Abstract: This study aims to investigate task complexity problems in attribute-based statedchoice valuation of non-monetary costs of automobile use. Two factors leading to taskcomplexity problems were considered: (1) non-linearity in the utility function indicating
uncertainty in preferences, and (2) parameterization of the scale of the stochastic error of theutility indicating decision complexity. Empirical investigations on the complexity of
non-market attributes valuation were made based on a web-based stated route choice surveyof work trips in Metro Manila. The results show strong suggestions that degree ofcomplexity of the choice problem is affected by not only the number of alternatives but also
by the range and the description of attributes.
Key Words: State choice, Preference uncertainty, Decision complexity

1. INTRODUCTION

Stated preference has been an important indicator in predicting values of traffic externalities
in the absence of markets. In addition, the valuation of non-market external costs ofautomobile use is a critical input in many of institutional evaluation and pricing policies
particularly in this age where the impacts of the transport sector, not only on the local but also
on the regional and global level, are becoming major environmental sustainability concerns.
Various issues surround the contextualization of these external costs for valuation. One ofthese issues is defining the external costs or benefits in terms of its quantitative dimensions
such as its quality level or in terms of its functional dimensions such as its role inenvironmental systems. Aside from that, dimensions of these externalities also vary
according to context. These add complexity in the process of valuation of the non-market
environmental goods associated with automobile use from stated choices.
Most applied stated choice survey has dealt with single dimension goods. These surveys
usually aim to get willingness-to-pay (WTP) for an environmental resource or amenity, sayvalue of clean air. However, most often, environmental systems cannot be simply
aggregated into a single good as most of them come naturally in bundles. Clean roadside air

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

quality, for instance, as a resource, may be broken down into attributes like visibility, impact
on roadside buildings, impact to soil and streams, and impact to human health. There are
two approaches in dealing with set of attributes, (1) Attribute-based Stated Choice Method(SCM), and (2) Contingent Valuation Method (CVM). Attribute-based SCM is an approach
where respondents are asked to choose from a set of alternatives with an array of attributes(Louviere et al., 2000). It is commonly analyzed using multinomial disaggregate modelsuch as the multinomial logit (MNL). In contrast, CVM elicits WTP value in open or closed
form on the context of made-believe circumstances that improve or damage to existing
environmental amenity. Environmental valuation studies using attribute-based SCM arelimited compared to CVM and other environmental valuation methods such as hedonic
pricing and travel cost. If price is the only attribute, the SCM simply reverts back to CVM.
There are some advantages in using attribute-based SCM over CVM: (1) the ability to
deduce behavioral tendencies of respondents over variation of goods or goods’ attributes; (2)policy implications are less suggestive, thus less susceptible to context biases or behavioral
heuristics (Herriges and Kling, 1999); and (3) avoid zero protest votes present in CVM(Wardman and Bristow, 2004).
This paper aimed to investigate the task complexity in situations where environmental goods
have varying goods dimension using empirical data. Though task complexity may be caused
by many factors within or outside the control of the decision-maker, we only focused on
factors affecting utility of the decision maker, i.e. preference uncertainty and decisioncomplexity. Section 2 describe the how the models were specified while section 3 describe an
empirical investigation using internet survey data on the valuation of the social costs ofmotor-vehicle use in Metro Manila. The result of the empirical test aims to contribute to thedesign of multi-attribute stated choice internet survey.

2. CHOICES IN MULTI-ATTRIBUTES ALTERNATIVES

The intricacy of non-market goods can often be captured by the broad array of the resource
attributes. The valuation of the external costs of automobile use can be likened to thevaluation of other environmental goods like, for example, preferences over an environmental
good such as forestland uses can be categorized into old-growth forest conservation,
hardwood native timber production, and recreation (Ananda and Herath, 2006). Anotherexample is policy involving economic and environmental systems which often consists ofmultiple objectives. Policies involving agricultural non-point source pollution may require
trade-off between contradicting objectives on soil erosion and water pollution(Lakshminarayan et al., 1995). The impacts of motorization on the environment likewisecomes in many dimensions such as air and noise pollutions, accidents, and environmental
decay (e.g. Sælensminde, 2001, Iraguen and de Dios Ortuzar, 2004). To address
multi-dimensionality of valuation problems, researchers have used multiple contingent
valuation (CV) (Protiere et al., 2004, Gonzalez and Leon, 2003, Feitelson et al., 1996,
Parumog et al., 2003) and choice experiments. While it has been argued that attributevaluation methods such as conjoint analysis provides a powerful alternative to CV, challenges
arises as to the complexity of decision-making.
This section presents two frameworks on how get inference on the preferences uncertainty of
the respondents and the decision complexity of the choice questions. Both these factors
influence the task complexity of the choice problems.

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2.1. Multi-attribute utility
Ecosystem valuation consists of a variety of environmental services or goods broken down
into attributes or objectives. Here, we introduce the multi-attribute utility (MAU) theory thatconsiders preferences over a range of attribute. Let us assume an environmental good q with
M attributes or services Q(q1 ,q2, …qM). This assumes that the subject has a well-behaved
utility function that follows U(x, Q, s, p). The utility is quasi-concave with respect to market
goods X, environmental good Q, and socioeconomic variables s. Debreu (1960) initiallyarticulated utility specifications with multi-attribute preferences in his discussion of goods
partitioning in stochastic choice models utilities while Fishburn (1964) did the same in termsof expected utility. Keeney (1993) extended the utility theory to describe decisions
involving preferential and utility independence, which decompose the multi-attribute utility
function to more practical form for elicitation.
The very flexible additive multi-attribute utility model based on the environmental good Q isdefined as in equation 1.
Mu (q 1 , q 2 ,...q M ) = ∑ k i u i q i

(1)

i =1

Additive utility assume that individual has strong preference independence. In cases,
however, where individual experiences uncertainty in preferences, multiplicative utility
structure persist. The kis are the scale parameters, u(qM) are the single-attribute utility
associated with qM. We follow Keeney and Raffia (1976) in showing that the condition ofpreference independence among attributes that presents the multiplicative multi-attribute
utility structure shown in equation 2.

(

M

( ) )

Ku (q , q ,...q ) + 1 = ∏ Kk i u i q i + 1
1

2

M

(2)

i =1

In this notation, K is the scaling constant that is a function of ki. The multiplicative utility
function can represent powerful preference structure as it can represent nonlinearities in utility
and interaction between attributes. Note that if K=0, then the utility goes back to equation 1.
The specification of the multi-attribute utility has great implications on environmental
valuation. Additive utility assumes preference independence and assume that values are
simply the marginal utility of attribute divided by the marginal utility of cost, which is simply
the utility parameter of attribute over costs. In case of multiplicative utility, the marginalutility becomes a function of the assumed function (e.g., quadratic) and interactions.
Uncertainty in preferences may be inferred from the functional form of the value functions.
2.2. Decision complexity
Simon (1957) presents the idea that consumer approach simplification of its cognitive burden
by making a decision based only on a part or selected attributes of an alternative. Most
choice-modeling framework assumes that respondents have perfect information-processing
capacity. Econometric models usually fail to acknowledge the common knowledge inbehavioral decision theory that choice environment, the inability of individual to makecomplex decision, and choice context affects decision-making.

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

A conventional random utility maximization (RUM) model relies on the random utility tointerpret preferences. The utility disturbances are the basis of the probabilistic inference onutilities. However, they are given very little consideration in the interpretation of the model.
Deterministic variables observable to the researchers are main concerns and the underlying
nature of the errors are critical to the explanation of the choice behavior are often overlooked.
Psychological models or behavioral decision theory touched on task complexity and theenvironment. Furthermore, more and more research incorporating decision-makers’ limitationin processing information about alternatives in choice problems and in econometric modelingare done.
In the following discussions, we follow Swait and Adamowics (2001) in presenting complex
decision and apply it to a multi-attribute discrete choice valuation framework. Their
proposed framework assumes a heteroscedastic discrete choice model where the variance orscale of the stochastic error is parameterized. The model presents a relaxation on the
neoclassical perfect decision-maker by incorporating assumption based on information theory.
With an individual maximizing his or her utility according to discrete commodities x withattributes q, prices p, and numeraire z, the following formulation based on Hanemann (1982)shows the utility maximization subject to: (1) budget constraint; (2) mutually exclusivealternative constraint; and (3) optimal quantity control.
MaxU ( x i ...x j , q i ...q j , z ) subject toJ∑p x +z=M
i =1

i

(3)

i

y i ⋅ y j = 0∀i ≠ j
y i = y i* ∀iThis framework reflects a perfect, utility maximizing, decision-maker and can be directly
implemented through conditional indirect utility function readily estimated through MNL.
In this framework, the respondent, in its task of making choices, exert an amount of effort tounderstand the context in terms of attribute levels of the alternative, and that when effort is
not applied, all attributes appear the same to the respondents. As effort directly depends on theindividuals, it represents the complexity of the choice environment. Swait and Adamowicz(2001) presented a way how integrate the complexity of the thinking process in the equation.
They assume an Ek representing the effort, where the k indexes the choice problem that the
respondents faces within the planning horizon identified as k=1 … K. The effort budget of
an individual is B. The quantities of B and E are unobservable latent variables. Hkrepresents the complexity of the task of choosing from k alternatives. The following
notations represent the consumer’s decision problem:
Max U ( y11... y j1 , q11...qJ 1 , E1H1;...; y1K ... yJk , q1K , qJK , EK H K ; z, EK +1 , H K +1 ) subject toJ∑py +z≤M
i =1

i i

(4)

K +1

∑E ≤ Bk =1kyik ⋅ y jk = 0∀i ≠ j
yik = yik* ∀i

By employing the random utility theory, with certain assumption of error distribution theprobability of choosing an alternative becomes:

97Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Pr( j = 1) =exp[µ i ( Ei H i ) ⋅ Vi1 ]∑ exp µ i ( Ei H i ) ⋅ Vij

[

]

(5)

j∈C

where µi is the scale factor related to scale factor that is inversely proportional to the error
variance in the RUM.
Complexity may be formally represented though the concept of entropy which is formalizedusing the information theory by Shannon (1948). This theory was applied initially in theprocessing language or information in communications. The theory assumes that aprobability distribution П= (π1, π2, π3,…πJ) has an associated entropy (or lack ofpredictability):J

H ( X ) = H (π x ) = − k ∑ π ( x j ) log π ( x j )

(6)

j =1

which is at maximum when all the probabilities, Hmax=ln(J), when all the probabilities havethe same values Pi=1/J and a minimum of 0 when the probability has a value of 1. The
complexity of the situation is assumed to affect the stochastic utility the term specifically its
variance a function of entropy of the decision. To capture nonlinearities in entropy, a quadratic
function is assumed for the entropy. The complexity function is formulated as:µ i ( Ei H i ) = µ n (C n ) = exp(θ1 H n+θ 2H n2 )where H n = − ∑ Q jn ln Q jnj∈Dand Qin =exp( β X in )for all i ∈ D∑ exp(βX in )

(7)

(8)

(9)

j∈D

Swait and Adamowicz (2001) infer that the these equations (4-9) allows the scale variance tocapture how a consumer make an effort up to a degree of certain complexity after which they
resort to simplifying decision heuristics which generate greater preference inconsistencies
across decision makers. If this assumptions were correct, θ1 ≤ 0 and θ2 ≥ 0, or opposite incase of the variance.

3. EMPIRICAL APPLICATION: MULTI-ATTRIBUTE ROUTE CHOICE

To investigate task complexity using empirical data, a stated preference route choice survey ofprivate work trips in Metro Manila is analyzed. A route choice experiment where, apartfrom the current route taken, two alternative routes with varying environmental improvements
in air and noise quality, greenery and streetscape, road safety. Attributes were assigned tovary randomly and stated choice problem is repeated six times. The choice is complicated asaside from deterministic attributes travel time and travel cost, subjective attributes such as thementioned environmental quality improvements were incorporated. We focused on
subjective variables corresponding to environmental qualities we wanted to investigate (i.e.
air pollution, noise, accident risk). As air quality is a complex concept to grasp when done98

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

in pollutant-specific manner, we present it in percentages of improvement. This readilytranslate into unit reductions or scaling of regional air quality index which is an aggregate
scale describing concentrations of major pollutants such as carbon monoxide (CO), sulphur
dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and fine particular matter in policy
context. Noise is likewise offered in percentages of reduction. Greenery and streetscapeare with or without scenario. In the current route, the number of fatalities a year is pegged at
150.
Table 1 shows how the attributes are determined for the hypothetical route alternatives.
Random noise is added to actual inputted travel time TT and actual travel cost TC. The
noise is deemed as a random draw from normal distribution with standard deviation that is
one-third the value of TT. On the other hand, noise for TC entails addition of the absolutevalue of a random draw from normal distribution with standard deviation that is one-third thevalue of TC. In the other attributes, embedded scripts performed random drawing of theattributes levels.
Table 1 Attributes levels
Attributes
Travel time:
Travel cost:
Air Quality Improvement:
Reduction in Noise Pollution:
Greenery and streetscape:Current
TT
TC
Base
Base
Base

Road fatalities/year:

150

Attribute levels
TT + R ~N(0,TT/3)
TC + | R ~N(0,TC/3) |20% improvement, 30% improvement, 80% improvement
20% reduction, 30% reduction, 80% reduction
greenery and streetscape improvements, greenery improvement,
streetscape improvement, and no improvements
20, 50, 75 and 100

In the six repetitions of the choice experiment, the dimensions of the attributes were varied toinvestigate changes in respondent preferences as the complexity of alternatives deepens. Table
2 shows the dimensions of the choice problem.
Table 2 Dimensions of the choice problems
Choicedim. type
SC 1
SC 2
SC 3
SC 4

No. ofAttributes
7
4
5
8

Obs.

Attributes

Repetitions

190
64
61
65

Price, Time, Air quality, Noise, Greenery, Streetscape, Fatalities
Price, Time, Air quality, Noise
Price, Time, Greenery, Streetscape, Fatalities
Price, Additional cost for environmental amenity, Time, Air
quality, Noise, Greenery, Streetscape, Fatalities

3
1
1
1

Total

6

Web survey of private work trips in Metro Manila was conducted for about three weeks, from
June 5 to July 1, 2005. Samples were drawn by sending e-mails to human resource
department heads of different private offices, government offices, non-government offices,
and institutions listed in various online directories. They were informed of the purpose andtimeframe of the study and were asked to forward the website to the personnel of their offices.
One follow-up email was sent for each request.
The questionnaire has five parts: work trip characteristics; environmental quality perception
in commonly used route; environmental attitudes; the experimental choice problems; and thesocioeconomic characteristics of respondents. Characteristics of the work trip asked include
home and work location, motor vehicle characteristics, travel time and cost to work. A
5-level scale of commonly used route’s environmental quality perception, which consists of
air pollution, noise pollution, greenery and streetscape, and road safety, as well as a 10-level
perception scale of the general environmental quality rating composed the second part of99

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

questionnaire. Ordinal rating questions on government spending on environment, stanceabout the environment, and subsidies made up the attitude questions. We received 83 filledquestionnaires, from which we have gathered 380 stated route choices of different dimensions
after eliminating lexicographic and unreasonable responses. The choice experiment format
SC 1 to SC 4 has 190, 64, 61, and 65 usable observations, respectively.

4.

RESULTS OF ANALYSIS AND FINDINGSThis section shows the results of the estimation of the models incorporating task complexity.
We estimated the basic MNL model with additive and multiplicative utility, and the MNL
considering task complexity. At first, we define a linear in-utility specification in the form:
V j =α j + β M X M + β TC X TC + ε j(10)Where the βM corresponds to the attribute parameter matrix and βTC is the price vector
parameter. The parameter estimates are presented in Table 3. The models show that
estimates are greatly affected by the number of samples since among the repetitions only
SCM1 is found to be robust. SCM3 follows with marginal significance mainly carried by
the variables related to traffic safety.
Table 3 MNL Estimates of attribute-based SCM with additive utility
Variable
1. Alternative 1 constant
2. Alternative 2 constant
3. Price (PhP)
4. Time (Minutes)
5. Air quality (1-% of improvement)
6. Noise (1-% of reduction)
7. Greenery (with or without)
8. Streetscape (with or without)
9. Number of fatalities a year
10. General rating of quality of road androadside (1 worst -10 best)
11. Income (1=>20,000, 0-otherwise)
No. of observationsA( β )

χ 2 (p-level)

ρ2
Estimated WTP
Travel time savings (in mins)
100% air pollution reduction
100% noise reduction
GreeneryLandscapeTraffic fatality reduction a year

SCM 1
1.497
(1.94)
1.277(1.62)-0.010 (-3.14)-0.043 (-4.36)-0.544 (-1.10)-0.095 (-0.22)
0.040(0.15)-0.287 (-0.86)-0.011 (-3.25)

SCM 2
SCM 3
0.447 (0.44) -0.600 (-0.30)
0.598 (0.54) -0.706 (-0.36)-0.024 (-2.90) -0.003 (-0.61)-0.059 (-4.01) -0.019 (-1.14)-0.490 (-0.43)-0.640 (-0.76)-0.054 (-0.09)-1.548 (-1.64)-0.020 (-3.60)

SCM 4
0.060 (0.04)
0.019 (0.01)-0.003 (-0.63)-0.040 (-3.98)
0.378 (0.47)-0.642 (-0.84)
0.103 (0.16)-0.723 (-1.25)-0.010 (-1.68)

0.340(3.34)

0.150 (0.94) 0.280 (1.20)

0.210-0.410
190
-170
125
0.27

(-1.07)0.070 (-0.12) 0.250 (0.35) -0.710 (-0.93)
64
61
65
-52
-54
-59
(0.000)7 (0.546) 28 (0.001)
13(0.323)
0.06
0.21
0.1

4.41
55.24
9.67
-4.07
29.2
1.11

(2.87)(1.13)(0.22)(-0.15)(0.86)(2.14)

2.47 (3.55)
20.68 (0.43)
27.01 (0.75)

5.6(0.85)(0.57)

13.53 (0.66)-128.04 (-0.42)
217.66 (0.51)
15.88 (0.09) -34.8 (-0.16)
455.1 (0.59) 245.21 (0.66)
5.76 (0.60) 3.29 (0.60)

Using the main effects variables [with subscripts TC (travel time), TC (travel cost), AI (airquality, NS (Noise), and AC (traffic fatalities)], we likewise estimated the multiplicative
utility function in the form to see interaction and nonlinear effects in the utility. In thisanalysis, we eliminated the variables landscape and greenery due to the low significant of
estimate and high correlation of the variables.

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

2
2
V j = α j + β1 X TT + β 2 X TT
+ β 3 X TC + β 4 X TC
+ β 5 X AI + β 6 X NS(11）2
+ β 7 X AI ⋅ X NS + β 8 X AC ⋅ + β 9 X AC + β 10 X AC

The estimates of the nonlinear in utility equation are shown in Table 4. Using the nonlinear
utility specifications, and the elimination of some variables, the fit of the models, except for
SCM 2, improved significantly (based on adjusted ρ2). It can be seen that while nonlinear
effects and interaction effects are insignificant in most models, it can be deduced that its effectin the general fit of the model is significant.
Table 4 MNL Estimates of SCM with multiplicative utility
Variable
Alternative 1 constant
Alternative 2 constantPrice (PhP)
Price (PhP)^2
Time (Minutes)
Time (Minutes)^2
Air quality (1-% ofimprovement)
8. Noise (1-% of reduction)
9. Air quality ⋅ Noise
10. Number of fatalities a year
11. Number of fatalities a year^2
12. General rating of quality of roadand roadside (1 worst -10 best)
13. Income (1=>20,000,
0-otherwise)
N
1.
2.
3.
4.
5.
6.
7.

A( β )

χ 2 (p-level)

ρ2
Travel time savings
100% air pollution reduction
100% noise reduction
Traffic fatality reduction a year

SCM 1
1.935
(2.29)
1.724(2.02)-0.015 (-2.34)
0.000(0.88)-0.060 (-2.55)
0.000(0.85)

SCM 2
0.609 (0.56)
0.719 (0.58)-0.032 (-1.97)
0.000 (0.51)-0.102 (-1.81)
0.000 (0.83)-1.434(-1.33)-0.573(-0.26)2.179(0.92)-0.9951.532-0.0080.000(-1.03)(0.95)(-7.53)(-0.27)-0.7200.066(-0.31)(0.02)(0.35)(-0.64)(0.81)(-1.29)0.321(3.20)

0.138-0.379(-0.99)

0.163

190
-169
127
0.27
3.98
24.69
-6.27
0.71

(0.000)(2.00)(0.26)(-0.08)(2.15)

64
-51
14
0.12
2.77
16.88
21.69

SCM 3
1.931
(1.30)
1.844(1.26)-0.012 (-0.83)
0.000(0.36)-0.005 (-0.12)
0.000 (-0.44)

SCM 4
0.364
(0.20)
0.346(0.19)-0.020 (-1.61)
0.000(0.99)-0.120 (-2.74)
0.001(2.38)-0.0370.000(-1.55)(0.79)

0.849-2.3370.018
0.000(0.86)

0.357(1.60)

0.245(0.20)(0.27)

0.239(0.33)-0.797(-0.93)(0.208)

61
-55
71
0.39
0.83

(0.000)2.48(0.52)(1.23)(0.24)(0.29)(0.24)

65
-57
48
0.29
5.59
-42.7939.68-0.14(0.000)(1.28)(-0.42)(0.37)(-0.13)To investigate the decision complexity, we estimated the models incorporating choicecomplexity in the equations (3-9). A quadratic form for the complexity function was used in
the estimation. Using the additive utility specifications, the results of models considering
choice complexity is shown in Table 5. In all models, the entropy parameters both at linear
and quadratic terms follows right signs for complex decision and are significant. SCM3,
with variables greenery and landscape, shows the highest scale of complexity followed by
SCM4 and SCM1 with eight and seven attributes, respectively. The value estimates tends tobecome less significant as the scale of the entropy parameters become bigger.

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Table 5 Estimates of MNL of SCM with complexity parameters
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

Variable
Alternative 1 constant
Alternative 2 constantPrice (PhP)
Time (Minutes¥)
Air quality (1-% ofimprovement)
Noise (1-% of reduction)
Greenery (with or without)
Streetscape (with orwithout)
Number of fatalities a year
General rating of quality ofroad and roadside (1 worst-10 best)
Income (1=>20,000,
0-otherwise)θ1
θ2
No. of observationsA( β )
χ 2 (p-level)

ρ

2

SCM1
5.135
(3.92)
4.902(3.82)-0.023(-6.50)-0.084(-8.90)SCM2
0.752(1.39)
0.683(1.27)-0.009(-1.79)-0.044(-2.39)-0.032(-0.05)0.254(0.52)-0.772-0.111(-2.04)(-0.44)-0.243(-0.84)-0.913(-2.03)-8.786(-2.20)-2.280(-2.19)-0.028(-7.53)-0.049(-6.17)-0.040(-3.92)

0.93(6.34)

0.11(1.39)

0.69(1.45)

0.72(1.40)-0.35(-0.98)0.28(1.13)

3.63(2.38)

0.34(0.28)

-6.47
7.64
190
-163
125

(-6.51)(6.45)

-3.56
4.64
64
-48
40

(-1.67)(2.13)-11.39
12.75
61
-50
42

(-2.23)(1.80)

-9.07
10.26
65
-57
48

(-1.62)(1.24)(0.000)0.28

Estimated WTP
Travel time savings
100% air pollution reduction
100% noise reduction
GreeneryLandscapeTraffic fatality reduction a year

3.67
1.40
33.80
4.85
39.96
1.21

SCM3
4.773
(1.36)
4.290(1.30)-0.016(-1.29)-0.071(-1.87)1.695(0.000)0.29(.78)(0.05)(1.94)(0.44)(2.07)(4.99)

4.85-27.6926.53(1.50)(0.001)

SCM 4
4.217
(1.26)
3.605(1.10)-0.019(-2.36)-0.062(-3.06)0.465(0.32)-0.186
0.979(-0.14)(0.98)

0.30(2.03)(-0.55)(0.88)(0.000)

0.29

4.34

(1.17)-103.52536.652.99(-1.10)(1.23)(1.27)

3.26-24.649.83-51.90120.832.09(1.87)(-0.32)(0.14)(-0.97)(2.01)(2.09)The Figure 1 below shows the entropy functions (equation 7) with respect to the probabilityof choosing the first alternative, or choosing current route and not the new alternatives withimproved environmental attributes. The trend is that, as complexity of the question
increases, i.e. less level of attributes, the respondent tends not to choose the improved
alternatives and choose the default alternative. The results also shows that choice problem
involving mainly non-use, dummy attributes such as greenery and landscape (i.e. SCM3)
appear to be more complex for respondents than attributes posing direct effects.
1.2
SCM1
1

SCM2
SCM3

Entropy

0.8

SCM4

0.6
0.4
0.2
0
0

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Probability of choosing current route

0.8

0.9

1

Figure 1 Entropy as a function of probability remaining in current route102

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5.

SUMMARY AND CONCLUSIONSIn this paper, we considered two factors leading to task complexity problems: (1)non-linearity in the utility function indicating uncertainty in preferences, and (2)parameterization of the scale of stochastic error of utility indicating decision complexity.
The results of estimations show presence of preference uncertainty and decision complexity inthe empirical application, which leads to inferior WTP estimates. Empirical application also
shows a strong suggestion that degree of complexity is affected not only by the number ofalternatives but also by the range and the description of the attribute.
Based on the results of this study, we identified some factors that should be considered in
preparing multi-attribute stated questions. First is the length or the number of attributes inthe design of choice alternatives. While results for up to four attributes are found acceptablein the empirical examples in this study, the number of attributes may vary according to thecomplexity of the good in question but attention should be taken in the specification of
attributes so as not to complicate tasks of choice-makers. Second is the range anddescription of attributes. From the experiment here, it is shown that attributes presented incrisp numbers, percentage or quantities, are better understood by the respondents thanqualitative descriptions. The dimension of the attribute levels is likewise a critical factor totake into account. The experiment has likewise shown that using random draws fromcontinuously distributed, time and cost attributes has produced robust parameter estimates. A
small sample pre-testing should be able to reveal the points in avoiding complexity in statedchoice valuation problems.

ACKNOWLEDGEMENT

We would like to acknowledge the invaluable comments of two anonymous reviewers. Allremaining errors are sole responsibility of the authors.

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