What Harvesters Really Think About in Commons DilemmaSimulations: A Grounded Theory Analysis

DONALD W. HINE and ROBERT GIFFORD, University of Victoria

AbstractVerbal protocols and structured interviews were collectedto investigate decision making in a simulated commonsdilemma. Grounded theory was used to identify the mainmotivational and cognitive mediators of harvest choicesduring the simulation. The core category that emergedfrom the analysis was labeled goal satisficing. Most par-ticipants adopted or formulated specific harvest goalsprior to and during the simulation. These goals guideddecision-making, influencing which strategies were em-ployed, and ultimately how many points were harvestedfrom the pool on each trial. Five action strategies thatparticipants used to pursue their goals were identified:developing initial harvest plans, monitoring pool size andothers' harvests, developing expectancies about others,simulating possible outcomes, and strategic influence.The results suggest that defection (resource overuse)occurs in commons dilemmas for two main reasons: afailure to adopt cooperative goals, or a failure to imple-ment effective action strategies after such goals areadopted.

ResumeDes protocoles verbaux et des entrevues dirigees ont eterecueillis pour examiner la prise de decision lors de di-lemmes communs simules. Nous avons utilise une theoriea base empirique pour determiner les mediateurs motiva-tionnels et cognitifs les plus importants dans les choix derecolte au cours de la simulation. L'analyse a permis depreciser une categorie principale appelee « atteindre lebut ». La plupart des participants ont adopte ou formuledes buts de recolte avant et durant la simulation. Ces butsont influence la prise de decision, pese sur les strategiesutilisees et finalement servi a mesurer le nombre de pointsrecoltes dans les stocks au cours de chacun des essais.Nous avons releve cinq strategies d'action utilisees par lesparticipants pour atteindre leurs buts : etablir des plansde recolte, surveiller la taille des stocks et les recoltes desautres, creer des attentes par rapport aux autres, simulerles resultats possibles et l'influence strategique. Les resul-tats suggerent que l'echec (la sur-utilisation de la res-

source) se produit dans les dilemmes communs pourdeux raisons principales : parce que Ton n'a pas adoptede buts cooperatifs communs ou que Ton n'a pas etablide strategies d'action efficaces une fois que ces buts sontadoptes.

Problems involving the management of shared re-sources, such as forests and fisheries, have long beenrecognized. Aristotle noted that "What is common to thegreatest number gets the least amount of care" (cited inWaldron, 1988, p. 6). This notion was elaborated byEnglish political economist W. F. Lloyd (1837/1968) in afamous parable about a group of herdsmen who grazetheir cattle on a shared pasture. According to Lloyd, eachherdsman is compelled by self-interest to increase thesize of his herd because he receives all of the benefitsassociated with adding cattle, but pays only a faction ofthe costs, which are distributed equally among all whoshare the pasture. The result is the inevitable destructionof the pasture, and ultimately the cattle that graze there.

Lloyd's parable was popularized by Garret Hardin(1968) in his seminal article, "The Tragedy of the Com-mons." Hardin's article has stimulated considerableinterest among social and environmental psychologists,who have generated a substantial body of researchaddressing the problem (for reviews see Dawes, 1980;Komorita & Parks, 1994; Messick & Brewer, 1983). Muchof this research has involved computerized simulationsin which participants, in small groups, manage a limitedshared resource over several harvest trials (Fusco, Bell,Jorgenson, & Smith, 1991; Gifford & Wells, 1991; Mosler,1993; Parker, Lui, Messick, Messick, Brewer, Kramer,Samuelson & Wilke, 1983; Summers, in press).

COGNITIVE MEDIATION IN COMMONS DILEMMASOver the years, there has been considerable interest inhow cognitive processes mediate harvest behaviour incommons dilemmas. Most research on cognitive media-tion has been loosely guided by three main theoreticalperspectives: limited processing theory, goal-expectancy

Canadian Journal of Behavioural Science, 1997, 29:3,179-193

Cognitive Mediation in Commons Dilemmas 181

theory, and three-factor theory. Each of these perspec-tives and relevant empirical findings are reviewed insections that follow.

Limited Processing TheoryLimited processing theory (Dawes, 1980) is based on thenotion that information processing limitations mayprevent harvesters from recognizing the long-termnegative consequences of adopting non-cooperativeharvest strategies. According to Dawes (1980), the com-plexity of most social dilemmas may prevent harvestersfrom fully comprehending the implications of theiractions. There is simply too much information to processin a short period of time. To cope harvesters typicallyattend only to the most salient aspects of the situation,which in the case of social dilemmas, is usually theimmediate pay-offs associated with cooperation anddefection. If only information about short-term pay-offsis processed, defection may appear to be the only sensibleoption available.

The limited processing perspective suggests threepotential solutions for increasing cooperation in com-mons dilemmas. First, it suggests that cooperation can beincreased by educating individuals about the long-termnegative effects of overharvesting and the benefits ofcooperation. A second possibility involves developinginterventions increase the salience of personal utilitiessuch as altruism, morality, and responsibility whichshould increase cooperation by diverting harvesters'attention from immediate pay-offs that favour defection.Finally, simply allowing harvesters more time to con-sider their harvest choices may increase cooperation,given that this would provide them with greater oppor-tunity to reflect upon both the nature of the dilemma andthe moral implications of their actions.

Empirical research support the effectiveness of thesefirst two interventions types, but not the third. Providingharvesters with dilemma-related information and strate-gies consistently produce increased cooperation andresource management efficiency (Edney & Harper, 1978;Rapoport, 1988; Schroeder, Jensen, Reed, Sullivan, &Schwab, 1983), as have interventions based on moralsuasion (Edney & Bell, 1983; Martichuski & Bell, 1991).However, the only study we could locate that examinedthe relationship between time to reflect and cooperationfailed to produce significant results (Dawes & Orbell,1982). Perhaps increased thinking time will only help ifharvesters are first primed to consider the long-term andmoral implications of their choices.

Goal-Expectation TheoryGoal-expectation theory (Pruitt and Kimmel, 1977) wasoriginally proposed to explain choice in 2-person pris-oner's dilemma games, but can be generalized to n-person situations such as commons dilemmas (e.g.,Yamagishi, 1986). According to the theory, two condi-

tions must be satisfied for cooperation to occur. Harvest-ers must (1) recognize that defection (i.e., overhar-vesting) is an untenable long-term strategy for maximiz-ing personal outcomes, and (2) expect that others in theirgroup will also harvest cooperatively. Thus, like limitedprocessing theory, goal-expectancy theory stresses theimportance of understanding the long-term benefitsassociated with adopting a cooperative strategy. But thisperspective emphasizes that mere understanding is notsufficient. Mutual trust among those who share theresource must also be present. For example, even ifharvesters recognize that restraint is the most viablelong-term strategy for maximizing their well-being, theyare unlikely to adopt this strategy if they believe thatothers will overharvest and extinguish the resource.

To date, most studies have focussed on the expectan-cies part of the theory, as opposed to the part that dealswith the formation of cooperative goals. Consistent withthe theory, most have found that harvesters who expectother group members to cooperate tend to cooperatethemselves, whereas those who expect others to defect(or overharvest) tend to defect (Dawes, 1980; Pruitt &Kimmel, 1977; Wilke & Braspenning, 1989). There is alsoevidence to suggest that expectancies may be tied tointerpersonal dispositions. Several studies report thatindividuals with cooperative social values tend to expecthigh levels of cooperation from others, whereas thosewith noncooperative social values tend to expect othersto cooperate less (e.g., Kramer, McClintock, & Messick,1986; Iiebrand, Wilke, Vogel, & Wolters, 1986; van Lange& Liebrand, 1991).

Three-Factor TheoryFinally, Samuelson and his colleagues (Messick et al.,1983; Samuelson & Messick, 1986a, 1986b; Samuelson,Messick, Rutte, & Wilke, 1984) have proposed a three-factor model to account for harvest decisions in com-mons dilemmas. According to the model, harvestdecisions are governed by three main motives: self-interest, a desire to use the resource wisely, and thedesire to conform to implicit group norms. The modelassumes that these motives will often conflict with eachother, and that the relative strength of each will varydepending on a host of contextual factors.

Most research suggests that the motives identified bythree-factor theory are indeed important determinants ofharvest choice. Several studies that examined the impactof resource-use feedback on harvest behaviour foundthat participants take significantly fewer resource unitsfor themselves when provided with feedback suggestingthe resource is being overused than when provided withfeedback suggesting underuse or optimal use (e.g., Rutte,Wilke, & Messick, 1987; Samuelson & Messick, 1986a;1986b; Samuelson et al., 1984), a result that is consistentwith the wise-pool-use motive and long-term self-interest. In terms of conformity, Samuelson et al. (1984)

182 Hine and Gifford

conducted an interesting study in which they manipu-lated conformity pressure by providing false feedbackabout the harvest behaviour of other group members.They found that participants tended to harvest fairlyclose to the group average when little variability waspresent in the harvest behaviour of the other groupmembers (high conformity pressure condition). How-ever, when other members' harvests were highly variable(i.e., when conformity pressure was absent), participantssubstantially increased their harvests. Subsequent studiesindicate that this conformity effect may be limited tosituations in which the resource is not being overused.Samuelson and Messick (1986a, 1986b) report that whenthe resource pool is being overused, participants tend toreduce their harvests regardless of the actions of theirfellow group members.

THE PRESENT STUDYThe primary aim of the present study is to employ aprocess-oriented approach to identify the main motiva-tional and cognitive factors that underlie harvest choicein commons dilemmas, and organize these factors into adescriptive/explanatory framework. Although this is notthe first study to investigate the mediating role of cogni-tion in commons dilemmas, it differs from previousstudies in several important respects.1. Past studies have employed questionnaires to mea-

sure motives and cognitions, or have attempted toinfer them indirectly from harvest behaviour. Thisstudy attempts to access the thought processes thatunderlie harvest choice more directly by employinga think-aloud method (Ericsson & Simon, 1980; 1984;van Someren, Barnard, & Sandberg, 1994) in whichharvesters verbalize their cognitions as they workthrough a resource management simulation.

2. Previous studies have tended to approach the prob-lem of cognitive mediation in commons dilemmasfrom a single theoretical (or atheoretical) perspec-tive, focusing on one or a few cognitive and motiva-tional variables in isolation. To date, little effort hasbeen made to integrate these variables into a broadertheoretical framework. For example, no one hasattempted to specify how limited processing mightaffect the selection and implementation of strategiesthat harvesters adopt in pursuit of their harvestgoals, or how expectancies might be linked to har-vest motives. The present study employs a moreholistic approach in which variables from a varietyof perspectives are investigated with the goal of atleast providing a basis for a more integrated perspec-tive.

3. Previous studies have tended to treated motives andcognition as static entities that do not change acrosstime. For example, in most goal-expectancy studiesexpectancies are measured only once — typicallyprior to the first harvest trial. Thus, little is known

about how expectancies develop and evolve inresponse to others' choices over the course of thegame. The same is true for most studies that haveinvestigated three-factor theory. The structuralfeatures of the simulation are assumed to make oneor more motives salient for the whole simulation.The issue whether motives shift across trials withinsimulations has not been systematically addressed.The process-oriented approach adopted in this studywill allow us to investigate these issues.

4. Finally, whereas most previous studies have beengeared toward testing hypotheses derived fromexisting theories, this study employs an analyticapproach specifically designed to develop newhypotheses and theoretical propositions. Thisapproach, known as grounded theory (Glaser &Strauss, 1967; Strauss & Corbin, 1990), begins withno a priori hypotheses and involves the inductivegeneration of categories and associations as theanalysis proceeds. A more detailed overview of theprocedure is presented in the methods section.

MethodPARTICIPANTS

Eleven females and five males served as participants.Eleven were undergraduate psychology students, threewere graduate students, one was a computer technician,and one was a high school student. Approximately halfthe undergraduates participated for additional coursecredit. All other participants were not remunerated,other than being given a chance to win their earnings ina lottery, the details of which are outlined later in thissection.

SOFTWARE AND HARDWAREThe resource-management simulation developed for thepresent study was written in Turbo Pascal, and was runon an IBM compatible personal computer. Harvestchoices were recorded by the computer and stored in adata file created by the program. The participants' verbalprotocols and post-experimental interviews were tapedby a portable cassette recorder adjacent to the computer.

PROCEDUREUpon arriving at the laboratory, the participants weregiven a brief written description of the study, and wereasked to sign a consent form. The participants were thengiven several warm-up exercises to familiarize them-selves with the think-aloud procedure. These exerciseswere adopted from Ericsson and Simon (1984), andinvolved thinking aloud while naming 20 animals, andrecalling the number of windows in one's house.

Following the warm-up exercises, the experimenterexplained the rules of the simulation to the participants.Participants were told that the task involved harvestingpoints (representing fish or trees) from a replenishable

Cognitive Mediation in Commons Dilemmas 183

TABLE 1Breakdown of Computer Player Harvests by Trial

Harvest Trial"

Programmed 1 2 3 4 5 6 7 8 9 10Responses

Green (non- 2 3 3 3 3 3 3 3 3 3cooperativeplayer)

Blue 2 2 1 1 1 1 2 3 3 3(cooperativeplayer)b

The initial pool size for the simulation was 12 points. The numberof points remaining after each trial doubled, up to a maximum of 12.On trials in which the programmed harvest exceeded the number ofpoints remaining in the pool, the computer players attempted to takeall the remaining points for themselves.bIn all but two cases, the pool was extinguished before Blue becamenon-cooperative (i.e., before Trial 8).

resource pool that they would share with two othergroup members, who would be simulated by the com-puter. They were told that the main goal of the exercisewas to acquire as many points for themselves as possibleover the course of the game, which would last for 10trials or until all the points were drained from the pool.They were also told that there may be more than one wayto maximize their personal point totals, and that therewas no 'one' correct way.

The resource pool initially contained 12 points, andeach group member was permitted to harvest 0,1,2, or 3points during each harvest trial. Following each trial, thenumber of points remaining in the pool doubled, up to amaximum of 12 points, the carrying capacity of the pool.After each trial, participants were provided with feed-back about the number of points harvested by each groupmember, the total number of points remaining in the poolprior to regeneration, and the number of points availablefor the upcoming trial. Feedback was presented in asummary table, which also included group members'harvests and pool sizes from all previous trials. This wasdone to help participants identify trends in others'responses and pool size over the course of the simula-tion. Participants were told that feedback would beprovided simultaneously to all group members, after theslowest member of the group had made his or herharvest decision. Thus, during any given trial, groupmembers (including the computer players) would notknow how many points others were taking on that trial,but would be provided with this information after thetrial had been completed. To preserve their anonymity,each group member was given a code name (Red, Green,and Blue). To increase motivation, participants were toldthat each point was worth $3, and that following thestudy a lottery would be held in which three individualswould win their actual earnings.

After outlining the rules of the exercise, the experi-menter produced a sample feedback form, identical tothe feedback screen used in the actual computerizedsimulation, and worked through three sample trials witheach participant (one in which the pool size declined,one in which the pool size remained stable, and one inwhich the pool size increased in size). Following thepractice session, the experimenter turned on the com-puter and started the simulation for the participant.Participants were instructed to proceed at their ownpace, and reminded to verbalize everything that theywere thinking as the completed the resource manage-ment task.

The introductory screen of the simulation restated therules and objectives of the resource management task.The second screen instructed the participant to turn onthe tape recorder and answer the following question: "Doyou have an initial action plan for maximizing yourpoint total during the resource management simulation?If yes, please describe it, even if it seems vague or incom-plete."

After answering this initial question, the participantswere prompted to begin the simulation. As noted earlier,the simulation was programmed to continue for 10 trialsor until there were no points remaining in the resourcepool. The responses of the Green and Blue (computer)players were preprogrammed to ensure that the poolwas extinguished prior to the tenth trial. The Greenplayer was programmed be generally non-cooperative(i.e., take many points) across trials, whereas the Blueplayer was programmed to harvest cooperatively. Abreakdown Green and Blue's preprogrammed responsesby trials is provided in Table 1.

Following the simulation, taped interviews wereconducted to supplement the protocol data. Participantswere asked to review the simulation trial by trial,recount what they were thinking as they made theirchoices. For cases 9 to 16, the post-experimental inter-views also included specific queries related to partici-pants' expectancies about others' choices becausepreliminary analyses revealed that expectancy referencesin the think-aloud protocols were often ambiguous anddifficult to interpret. After completing the post-experi-mental interview, participants were fully debriefed andthanked for participating.

GROUNDED THEORY ANALYSISThe verbal protocols and interview transcripts wereanalyzed using grounded theory, a systematic approachfor generating theory from qualitative data. The proce-dure was developed by two sociologists, Glaser andStrauss (1967), in response to what they considered to bean overemphasis on 'armchair-deductive' theorizing inthe social sciences. The approach involves severaldistinct phases (open coding, axial coding, selectivecoding, and integration), which are outlined in the

184 Hine and Gifford

TABLE 2Inter-Rater Reliabilities for Main Coding Categories

Category

Harvest PatternInitial Harvest Plan3

Monitoring"Expectancies'"Simulated Outcomes"Strategic Influence

% Agreement

949295919199

Kappa

.89

.79

.86

.86

.79

.92

"The subcategories associated with these action strategies were notmutually exclusive, so separate inter-rater reliability analyses werecomputed for each. The values reported in the table for these actionstrategies were computed by averaging the reliability scores acrosssub-categories.b For expectancies, reliabilities were computed separately for cases1 to 8 and cases 9 to 16, given that only that latter group wereexplicitly asked about expectancies in the post-experimentalquestionnaire. Coding was highly reliable for both sets (percentagreement > 88, K >.83). The table entries for this category are meansaveraged over both sets.

sections that follow. More detailed overviews of theprocedure can be found elsewhere (e.g., Henwood &Pidgeon, 1992; Rennie, Phillips, & Quartaro, 1988; Strauss& Corbin, 1990).

Open CodingOpen coding involves inductively generating categoriesfrom the transcribed verbal protocols and interviews. Tohelp ensure that one's capacity to generate is not con-strained by past knowledge or theoretical biases, theresearcher should have no pre-set or valued hypothesesduring this initial stage (Glaser & Strauss, 1967). Themethod of constant comparison serves as the key guidingprinciple during open coding. According to Glaser andStrauss (1967, p. 114),

the basic defining rule for the constant comparativemethod (is) while coding an incident for a category, com-pare it with previous incidents in the same and differentgroups coded in the same category.

Categories, once specified, are not permanent orunchangeable. At the beginning of the analysis, mostcategories tend to be fairly descriptive. As the analysisproceeds, and the model or theory begins to develop,these initial descriptive categories tend to be collapsedinto more abstract or conceptual categories (Strauss &Corbin, 1990).

Axial CodingDuring axial coding, an attempt is made to organize thecategories into a preliminary theoretical framework(Strauss & Corbin, 1990). During this phase, one alter-nates between inductive and deductive reasoning;initially making hypotheses about how certain categories

might be related, and then returning to the data to testthe proposed relations. In grounded theory, the notion of"hypothesis testing" is used more loosely (or at leastdifferently) than in traditional quantitative psychology.Tests of statistical significance are rare in groundedtheory studies, partly for philosophical reasons andpartly because most studies of this type involve rela-tively few subjects. Thus, "hypothesis testing" in thepresent context means returning to the data to determinewhether they are consistent, in a general sense, with therelations proposed by the evolving model or theory.

Selective Coding and Theoretical SamplingDuring selective coding, the researcher strives to identifya single core category which describes the centralprocess or phenomenon under investigation, and towhich all other categories in the framework are tied(Strauss & Corbin, 1990). As the model or theory beginsto unfold, it usually becomes evident that certain catego-ries are underdeveloped or in need of further refinement.For example, in some instances little or no variabilitymay be present in a category that the researcher believesis critical to the phenomenon under investigation. Insuch cases, an attempt may be made to selectivelysample participants or context to ensure that sufficientvariability is obtained. This practice is referred to astheoretical sampling (Strauss & Corbin, 1990) or theory-based sampling (Rennie et al., 1988) because data collec-tion is guided by theoretical as opposed to statisticalconcerns.

IntegrationThe final stage in a grounded theory analysis involvesintegrating one's findings with the empirical and theoret-ical literature. Ideally, the new model or theory shouldprovide a new perspective for interpreting the literature,help clarify contentious issues, and suggest directions forfuture research (Strauss & Corbin, 1990).

CODING RULESFor the present study, the coding rules for the maincategories were developed by the author, and then usedby two independent raters to code the data set. Twoindices of inter-rater reliability were computed for eachcategory: (1) percent agreement (PA) - the percentage ofclassifications on which the coders agreed), and (2)Kappa (k) - a reliability index for nominal scales thatcorrects for chance agreement among coders (Cohen,1960). A breakdown of these values by category ispresented in Table 2. Most coding disagreements arosefrom obvious oversights on the part of one coder or theother, and were easily resolved through discussion. Inrare instances in which disagreements could not beresolved, protocols were coded as not reflecting thecategory or theme in question. A more detailed descrip-tion of the specific coding rules used for each category is

Cognitive Mediation in Commons Dilemmas 185

provided in the results section.

Results and DiscussionHARVEST CHOICES

On average, participants harvested 1.80 (SD = 0.55) pointsper trial, managed the resource pool for 4.94 (SD = 1.57)trials, and acquired 8.56 (SD = 2.00) points over the courseof the simulation. Four harvest patterns were identified.Participants who decreased their harvests as pool sizedeclined were classified as cooperative (n = 9). Partici-pants who consistently overharvested across trials wereclassified as noncooperative (n = 4). Those who initiallycooperated (i.e., decreased their harvests as the pooldeclined), but increased their harvests late in the simula-tion were classified as late-trial defectors (n = 2) Andfinally, participants who did not conform to any of thesepatterns was classified as other (n = 1).

THE CORE CATEGORY: GOAL SATISFICINGAs noted earlier, the core category in grounded theory isthe central process or phenomenon around which allother categories are integrated. In the present study, thiscategory was labeled goal satisficing. Participants almostalways adopted or formulated specific harvest goalsprior to and during the simulation, and then imple-mented various cognitive and behavioural strategies inan attempt to fulfill these goals. Given that these strate-gies were rarely critically evaluated or systematicallyapplied during the simulation, goal satisficing wasconsidered a more accurate description of this processthan similar labels such as goal optimization or goalstriving. The term satisificing suggests that althoughbehaviour is goal directed, harvesters typically onlyallocate sufficient cognitive resources to ensure accept-able, as opposed to optimal, outcomes. As an example,only one participant (the high school student) systemati-cally evaluated several harvest plans before settling onone that he believed would help him maximize hispersonal point total. Most others evaluated a singlestrategy, deemed that it might work, and proceeded withthe simulation.

In this study, harvest goals appeared to arise from twomain sources: the experimenter, and the participantsthemselves. During the instruction period preceding thesimulation, participants were instructed that the mainobjective of the simulation was to acquire as many pointsfor themselves over the course of the game. A review ofthe transcripts revealed that with one exception, allsubjects (Ss) accepted the point-maximization goal asframed by the experimental instructions. "I was teeteringbetween taking one and two [points], but the point of thething was to maximize my grab so I went for two points."[SI] "What I was trying to do was to get everyone to taketwo each time, which is most efficient. Then we'd all endup with 20 instead of now when we all end up with a lot

less." [S4] "I was just trying to maximize my points,which is what I was told to do." [S5]

Goals other than point maximization were also oftenadopted (these will be discussed and quantified insubsequent sections). In some instances, participantsdevised strategic sub-goals that they believed wouldhelp them maximize their points (e.g., maintaining thepool for as many trials as possible, and eliciting coopera-tive responses from others). In other instances, partici-pants formulated goals that were more or less orthogo-nal to point-maximization. One participant, for example,indicated that in addition to maximizing her points, shealso wanted to 'stay ahead' of her competitors, a strategyconsistent with McClintock's (1972) conception of acompetitive social value orientation.

Although most participants added goals that weresubsidiary to, or at least consistent with, personal point-maximization, one participant rejected this goal alto-gether and replaced it with a new goal that was moreconsistent with her moral orientation.

I believe in the interdependence of us all and... if I maxi-mize my point count then someone else is going to beminimized, and ultimately I believe that is going to hurtme. And if not me, it's certainly going to hurt my chil-dren and grandchildren, and people down the road. Andso the whole notion of maximizing at some else's expenseis repugnant to me and has long been repugnant to me.I'm much more interested in maximizing for everyone;some sort of cooperative way of maximizing what every-body gets. I'm prepared to take a little less if I can be surethat other people get a fair share too. [S3]

Thus, even though the objective of the simulation hadbeen clearly outlined, this did not ensure that all partici-pants adopted it, nor did it prevent additional goals frombeing formulated.

COGNITIVE AND BEHAVIOURAL STRATEGIESFOR POINT-MAXIMIZATION

We identified five main cognitive and behaviouralstrategies that participants employed in an attempt tomaximize their point totals and accomplish their otherharvest goals. These included: developing an initialharvest plan, monitoring pool size and others' behav-iour, developing expectancies about others, simulatingpossible outcomes, and attempting to influence others'harvest choices.

Initial Harvest PlansPrior to the simulation, participants were asked by thecomputer whether or not they had developed an initialharvest plan to maximize their point totals. Three-quarters of the sample were coded as having an initial

186 Hine and Gifford

plan.1 The most common point-maximization strategyverbalized was to maintain the resource pool at a reason-ably high level to ensure that the simulation lasted aslong as possible (n = 6). Participants who adopted thisstrategy recognized that they could acquire more pointsfor themselves in the long run by sustaining the resourcerather than by extinguishing it quickly. Another commonstrategy involved closely monitoring the resource andothers' choices, and adjusting one's harvest fit the needsof the situation (n = 5). Although none of the participantswith this monitor-and-adjust strategy explicitly statedthat they intended to make adjustments that would helppreserve the resource, most displayed cooperativeharvest patterns, suggesting that pool preservation wasimplicit in their adjustment strategies.

Interestingly, only two participants raised the issuesof fairness and equitable distribution of the resource intheir initial plans. We found this surprising given thatseveral recent studies have suggested that equal divisionconstitutes a commonly applied standard in resourcesharing exercises (Allison & Messick, 1990; Allison,McQueen, & Schaerfl, 1992; Samuelson & Allison, 1984).The scarcity of fairness and equity references in ourstudy may be partly attributable to how the simulationwas framed. Presenting the simulation as a point-maxi-mization task, as opposed to a resource-sharing task or agroup management task, may have prevented partici-pants from considering fairness and equity to be relevant.

Four participants were unable to articulate an initialharvest strategy when prompted by the computer. All ofthese appeared to be cognitively overwhelmed by thesimulation, either reporting that they felt confused ordisplaying obvious symptoms of confusion (e.g., attempt-ing to harvest more than the allowable number of pointsfrom the pool during the initial trial). Not having aninitial strategy was also linked to high initial harvests.Three of the four participants, without a harvest plan,none of whom had competitive harvest goals, harvestedthe maximum allowable number of points during thefirst trial of the simulation, as opposed to only three oftwelve who had a plan. This suggests that when harvest-ers are confused and unclear about how to proceed, theymay be more likely to pursue their point-maximizationgoals through the most obvious means available, in thiscase by harvesting as many points from the pool as therules allowed. This interpretation is consistent withDawes' (1980) limited processing perspective.

1 Participants who indicated only that they intended to wait andsee what others would do were coded as not having an initialharvest plan, whereas participants who indicated that theyintended to do something over and beyond waiting and watchingwere coded as having one.

MonitoringThroughout the simulation, most participants closelyattended to feedback about pool size and others' har-vests. Consistent with the goal-satisficing perspective,those aspects of the simulation most relevant to pointmaximization and pool preservation were monitoredmost closely.

Monitoring Pool Size. Explicit references to pool size werecoded for 61% of all harvest trials, indicating thatparticipants regularly attended to this feature of thesimulation. Moderate levels of pool size monitoringoccurred during the first two trials of the simulation (M= 43%). However, as the resource began to decline,attention to pool size increased and remained high forthe remainder of the simulation (M = 72% for trials 3through 8).

Pool-depletion feedback was strongly related toharvest choice. The Pearson correlation between averagepoints taken per trial and average points available pertrial was r (6) = .95; as the number of points in the pooldecreased, participants reduced their harvests substan-tially. Thus, participants not only attended to pool size,but also likely adjusted their harvests in response to thiscue.

The association between pool size and points takenfound in this study is much larger than reported inprevious studies (e.g., Messick et al., 1983; Samuelson &Messick, 1986a; Samuelson et al., 1984). One possibleexplanation for this discrepancy is that the present studyemployed a small initial resource pool that declinedquickly across trials, whereas most past pool-depletionstudies have used larger initial resource pools, oftenconsisting of hundreds of points, that declined moreslowly. Given that the immediate threat of extinguishingthe resource was more salient in our study, participantsmay have been more motivated to reduce their harvests.A second intriguing possibility is that the "act" of think-ing aloud may have increased harvester restraint as thepool declined. Perhaps when individuals are required toverbalize their goals, they consider this to be similar tomaking a public commitment and feel bound to act in amanner that is consistent with those goals. Recall thatmost participants who indicated that they intended topreserve the resource pool adhered to this strategythroughout the simulation. In studies in which thinkingaloud is not required, participants may feel less obli-gated to remain committed to their original goals andstrategies.

Monitoring Others' Harvests. In addition to pool size,most participants also paid close attention to the harvestchoices made by other members of their group. Explicitreferences to other group members' harvests werepresent for 58% of all harvest trials. Participants tended

Cognitive Mediation in Commons Dilemmas 187

to monitor the choices of both Blue (the cooperativecomputer player) and Green (the noncooperative com-puter player) during the early trials of the simulation;72% of all references to other's harvests during trials 2and 3 were to both Blue and Green. However, as the poolbegan to decline most shifted their attention exclusivelyto Green (82% of all references during trials 5 through 8)."This Green participant seems really greedy, so the poolis going down instead of up." [S8] "Green took a lotagain... If Green keeps taking more, then we're going torun out and not have much more to take from." [S7] Thisshifting pattern of attention is consistent with the viewthat participants monitor features of the simulation thatare most relevant to their harvest goals. Given that Bluerepeatedly cooperated across trials, (s)he was not per-ceived to be a threat to the resource or an obstacle to poolpreservation. Green's choices, on the other hand, wereinconsistent with preservation, and therefore warrantedmore attention.

Monitoring others' harvests sometimes involved anelement of self-other comparison (comparisons occurredin 14% of all trials). "OK, it appears that they are notgoing to be as greedy as I am." [SI] I got three, and theygot two each." [S3] "We all chose two. That seemed towork out really well." [S7] Self-other comparisonsoccurred most frequently directly following the initialtrial of the simulation; 47% of all participants comparedtheir harvests with others following this trial. On subse-quent trials, such comparisons were very rare, occurringon only 8% of all remaining trials. We interpreted this ameaning that self-other comparisons may primarily serveas a general orientation function. Participants who areuncertain about how to choose early in the simulationmay compare their choices with others to determinewhether they were responding appropriately.

Monitoring Cumulative Point Totals. Explicit self-othercomparisons of cumulative point totals were relativelyrare, occurring on only 5% of all trials. Once again, thisis consistent with the interpretation that participants'goals are an important determinant of which features ofthe simulation are monitored most closely. Given thatfew participants expressed an interest in acquiring morepoints than others or ensuring that the points in the poolwere equally distributed, it makes sense that few point-total comparisons were made. Differences in self-otherpoint totals simply were not relevant to most partici-pants.

Is there a relation between self-other point-total com-parisons and harvest choice? It seems reasonable thatunfavorable comparisons may serve as a precursor todefection. For example, when participants realize thatthey have acquired fewer points than others, a naturalinclination might be to increase their harvests to narrowthe difference. No evidence was found to support thishypothesis. Two cases were identified in which unfavor-

able point-total comparisons were made, and in bothcases participants reduced their harvests on the subse-quent trials. In general, participants appeared to be moreconcerned with preserving the resource than ensuringthat points were allocated equally. The following excerptis consistent with this interpretation.

The Green one who has been taking three now has eight.I have five and that other person [Blue] also has five. Andwe're not getting anywhere because the pool size is de-creasing at all times. OK there's only two points left. Wellif there's only two points left. One person is dominatingthe whole show here. But that's no reason, it's a limitedresource, so that's still no reason to increase what I'mtaking. [S3]

Developing ExpectanciesIn commons dilemmas, personal outcomes are depend-ent not only on one's own harvest choices, but also onthe choices made by the other members of the group.Given this latter dependency, it is clearly advantageousto develop expectancies about how others will act, andto use this information to guide one's choices.2

Initial Expectancies. When the simulation began, seven ofthe eight participants who could be classified indicatedthat they had not yet developed expectancies about howothers in their group would harvest. As the simulationprogressed, however, all participants developed expec-tancies about others. In general, expectancies wereconsistent with the feedback provided about others'choices (i.e., most participants came to expect Green, thenon-cooperative player, to defect and Blue, the coopera-tive player, to cooperate). The high correspondencebetween feedback and expectancies suggests that mostparticipants acted like trait theorists, and assumed thatpast behaviour would be a useful predictor of futurebehaviour. They carefully monitored others' harvests,attempted to identify a pattern, and based their expectan-cies on that pattern.

Inconsistencies between feedback and expectanciesarose, but only occasionally. In four instances, partici-

2 Expectancies were coded directly from the verbal protocols forcases 1 to 8. For cases 9 to 16, participants were asked about theirexpectancies during the post-experimental interview. Participantswho indicated that they did not know how Green or Blue wouldchoose were coded as having no expectancy. Statements such as"I'll take two, and see how others react" were also coded as noexpectancy. Statements in which participants who indicated thatthey expected Green or Blue to "take three", "take a lot", "notcooperate", "increase his harvest", etc. were coded as expectsdefection. Statements in which participants who indicated theyexpected Green or Blue to "take 1 or 2", "take few points","cooperate", etc. were coded as expects cooperation. Instances inwhich it was unclear whether participants had expectancies werecoded as unclassifiable (45% of all trials).

188 Hine and Gifford

pants indicated that they expected Green to cooperate,even after receiving feedback that Green had defected forseveral trials in succession. These participants appear tobase their expectancies primarily on situational factorsrather than others' past behaviour, "...so that lowered thepoints to eight. I thought for sure that Green would dropthe points, but he or she didn't [S2].° "I could see the poolwas getting low in size and I didn't want to take anymore points at that point. And I expected my competitorsto do the same, but... [S6]." "Basically the reason I did thatwas just to get the pool back up to a higher size. I didn'tthink Green would take three again. [S16]" In short, theytended to act more like prototypical social psychologists,believing that Green's predisposition to defect would beoverwhelmed by situational factors.

Expectancies and Harvest Choice. Consistent with previousfindings (e.g., Dawes, 1980; Pruitt & Kimmel, 1977; Wilke& Braspenning, 1989), participants tended to take fewerpoints from the pool when they expected both othermembers of their group to cooperate (M^^^ = 1.2, SD =.84, Range = 0,2) and take more points (typically themaximum allowable) when they expected both others todefect (M,^,^ = 2.75, SD = .50, Range = 2,3). On trials inwhich participants expected only Green to defect, harvestchoices were less uniform; some restrained their harvests,whereas others did not (M^^, = 1.56, SD = 1.15, Range =0,3). The protocols suggest that all those who cooperatedwhen they expected Green to defect believed that theirlong-term interests would be best served by preservingthe pool. In addition, several hoped that if they kept thepool alive. Green would eventually reduce its harvest. "Ithink I'll take one again and hope that Green will noticethat it's taking everything." [S4] "I wonder if I tried it[taking zero] one more time. Is that being foolish? Is thatbeing a patsy or is that giving the other guys a chance tosee the light?" [S3] "I thought that maybe if I took less,maybe the other competitors would take less, and there-fore increase the pool." [S10] This suggests that partici-pants' immediate expectancies about Green were not acritical determinant of harvest choice in these cases. Ofgreater importance was the belief or hope that Greenwould eventually cooperate before the resource wasextinguished.

Four participants, who initially attempted to preservethe resource pool, increased their harvests late in thesimulation despite expecting Green to continue to defect.The main precipitating factor of these late-trial defectionsappears to be lost hope. They realized that Green wasunlikely to ever cooperate, and as a consequence, decidedto take as many points for themselves before the poolwas extinguished. "Well if there's two left, I may as welltake two, otherwise I won't get anything..." [S4] "Ithought that I'm going to take three because I figured thatbecause it always took three that it would probably take

three again. I thought that I better get some points inhere." [Sll]

Methodological Issues Related to the Study of Expectancies.Our results raise several important methodologicalissues related to the study of expectancies in commonsdilemmas. As noted in the introduction, most previousresearch has ignored the important issue of how expec-tancies develop and can be shaped over time. Severalstudies (e.g., Dawes, McTavish & Shaklee, 1977; vanLange & Liebrand, 1991) have employed single-trialgames, and therefore ignored the temporal dimensionaltogether. Others have examined behaviour over severaltrials, but have included only one expectancy measure atthe beginning of the simulation (e.g., Kramer et al., 1986;Messick et al., 1983; Wilke & Braspenning, 1989). Thislatter approach implicitly assumes (a) that participantswill have well-developed expectancies about othersbefore the simulation begins, and (b) that expectancies,once reported, will remain stable over time.

Results from the present study suggest that both ofthese assumptions may be incorrect. As noted previ-ously, many participants indicated that they had no ideahow others would choose on the initial trial of thesimulation. Furthermore, after expectancies had devel-oped, they were not always stable across trials. Forexample, several participants who expected Green tocooperate early in the simulation changed their expec-tancies as the game progressed.

Most previous studies have measured generalizedexpectancies about others. For example, Messick et al.(1983) posed the following question to their participants:"Suppose that you decided to voluntarily restrict thenumber of points you took per trial. How likely do youthink that the other group members would respondsimilarly?" Kramer et al. (1986) asked their participantsto "estimate how many points the others would take onaverage from the common pool." One important differ-ence between the present study and previous studies isthat this study measured expectancies about specificothers as opposed to others in general. Our resultssuggest that the distinction between generalized andspecific expectancy measures is an important one. Whenothers do not respond uniformly, participants oftendevelop different expectancies for each member of thegroup. Furthermore, these different expectancy combina-tions appear to be related to harvest choice. Recall thatparticipants tended to defect when they expected bothothers to defect, cooperate when they expected bothothers to cooperate, but did not respond uniformly whenthey had different expectancies for Blue and Green.

Many past studies have employed measures that askrespondents whether they think others in their groupwill cooperate or defect, but do not include a 'no expec-tancy' response option (e.g., Kramer et al., 1986; Messick

Cognitive Mediation in Commons Dilemmas 189

et al., 1983; van Lange & Liebrand, 1991). This approachis problematic because it forces participants to reportexpectancies even if they don't have any. The open-endedapproach employed in this study allowed for moreflexible responding and provided us with fresh insightsinto how expectancies develop in response to feedbackabout others harvests.

Finally, our results suggest that a distinction should bemade between immediate and long-term expectancies.Many participants in the present study continued tocooperate when they expected Green to defect on theupcoming trial, but only if they believed or hoped thatGreen would eventually cooperate.

Simulating Possible OutcomesPrior to actually making their harvest decisions, partici-pants often mentally simulated the potential conse-quences of their own choices and the choices of others.3In terms of the emerging framework, this was interpretedas a cognitive strategy for increasing the probability ofgoal-consistent harvest choices.

Participants simulated possible outcomes on justunder half (44%) of all harvest trials. Mental simulationswere relatively infrequent during the early trials of thesimulation (M = 27% of the time for trials 1 and 2), butbecame more frequent as the pool declined during themiddle and late stages of the game (M = 59% of the timefor trials 3 through 8).

We can think of two plausible explanations to accountfor this trend. First, harvesters may become more moti-vated to consider the possible consequences of theirchoices in situations in which they perceived theirharvest goals and strategic sub-goals (i.e., point-maximi-zation through the preservation of the resource pool) tobe threatened. A second possibility is that participantsmay have been reluctant to simulate possible outcomesearly in the simulation because they were uncertainabout the harvest intentions of the other group members.Given that the outcome of each harvest trial depends onthe combined choices of all group members, participantsmay have been unwilling to waste their time speculatingabout future outcomes when they had no idea howothers would act.

3 Mental simulations were coded directly from the verbalprotocols. To be coded as a mental simulation statements had to bephrased in "if-then" (cause-effect) terms, or be easily translatedinto such terms without altering the statement's meaning.Furthermore, the statement had to refer to a specific actor oractors, an action, and consequence. For example, the phrase "Ithink I'll only take one point this time so the pool will get larger onthe next turn" meets the criteria because it refers to a specific actor(the self), action (taking one point), and consequence (pool getslarger). On the other hand, the phrase "I'll take three and see whathappens" does not meet the criteria. Although the phrase containsand actor and action, it does not contain a definite consequence.

Number of Simulations. How many possible outcomes aretypically simulated before a harvest decision is made?When we began the study, we envisioned participantssystematically working through several alternatives andselecting the one that promised to produce the mostfavorable outcome. We found little in the protocols tosupport this view. The vast majority of participantssimulated a single outcome, deemed the outcomeacceptable or unacceptable, and then harvested accord-ingly. The possibility that others might not conform tothe participants' projections was generally not consid-ered, nor was the possibility that alternative choicesmight produce more optimal outcomes. The followingexcerpts can be considered typical. "So if we all take 2points, that will leave 6 points, and that would double toleave 12." [S2] "OK, so now there are only 6 [pointsremaining]. Oh we want to get that to 12. If everyonetook zero, that would work." [S3]

It is not clear why most participants simulated so fewoutcomes prior to making their choices, but severalexplanations seem plausible. First, many participantsmay have been cognitively overwhelmed by the de-mands of learning a new game, keeping track of thevarious types of feedback provided, and thinking aloud.The stress of being in a novel situation, and the fear ofbeing evaluated by the experimenter may have furtherhindered their ability to process and organize informa-tion efficiently. A second, more motivational, explana-tion is that participants were capable of, but unwilling toexpend the cognitive effort necessary to evaluate alterna-tive strategies. Perhaps the relatively remote chance ofwinning one's earnings in the lottery was considered notto be worth the effort, although there was nothing in theprotocols to suggest that this was the case. A thirdpossibility is that the superficial processing reflected inthe protocols accurately depicts how most individualsmake decisions most of the time. This last explanation isconsistent with Dawes' (1980) limited processing per-spective, and also much of the work on cognitiveheuristics reported in the social cognition literature (e.g.,Nisbett & Ross, 1980).

Mental Simulations and Harvest Choice. As noted earlier,our framework suggests that harvesters use mentalsimulations as a strategy to increase the likelihood thatthey will make choices that are congruent with theirharvest goals. Given that most of the participants in oursample, adopted strategic sub-goals that involvedconserving the resource pool, one might expect thosewho engaged in more mental simulations (i.e., thoughtmore about the implications of their choices) to harvestfewer points per trial and manage the resource moreeffectively than those who engaged in fewer simulations.To explore this proposition, Pearson correlations werecomputed between the proportion of trials containingsimulations and two harvest indicators (number of trials

190 Hine and Gifford

played and average harvest per trial). Participants whosimulated more kept the resource pool alive longer (r =.54) and harvested fewer points per trial (r = -.74) thanthose who simulated less.

Although the previous analysis suggests a strongpositive association between simulation rate and harvestrestraint, this result is primarily attributable to the largenumber of participants in our sample who adopted pool-preservation sub-goals. High rates of mental simulationmay help harvesters achieve their goals, but they havelittle effect on which goals are adopted. Had the majorityof our respondents adopted non-cooperative harvestgoals (e.g., acquiring more points than their fellow groupmembers), we predict more mental simulation would beassociated with more overharvesting, not less.

We should also point out that, in certain instances,mental simulations may result in overharvesting even ifthe simulator intends to preserve the resource pool. Thiscan occur if the simulation involves faulty logic, baddata, or a mathematical miscalculation. For example, onthe second trial of the simulation, Participant 3 indicatedthat she intended to replenish the pool back to its maxi-mum level ("So to maximize you want to get that up to 12next time."). But when she computed how many points totake, she assumed that there were 12 points availablewhen, in fact, there were only 10. As a result she ex-ceeded the optimal harvest level, and the pool declined."There's only three points left. How come... oh becausethere was only ten to begin with. Oh, I miscalculated.That was stupid." [S3] This suggests that simple process-ing errors can lead even cooperative-minded participantsto overharvest.

Strategic InfluenceStrategic influence refers to explicit attempts by partici-pants to influence the choices of other group membersduring the simulation.4 In theory, strategic influence cantake a variety of forms: verbal exchanges, dirty looks,signed agreements, etc. However, in the present studydirect communication among harvesters was not permit-ted, which significantly reduced the possible modes ofinfluence participants could employ. Nevertheless,almost one-third of the sample (five participants) en-gaged in strategic influence attempts. In all five cases,participants attempted to influence others' choicesindirectly by modelling the behaviour they wantedothers to engage in; participants harvested few pointsfrom the resource pool, hoping that Blue and Greenwould reciprocate. "I thought that maybe if I took less,maybe the other competitors would take less."[S10]

4 Strategic influence was coded from both the verbal protocols andthe post-experimental interviews. Statements that unambiguouslyindicated that participants intentionally used their own harvests toinfluence the choices of others were coded as strategic influence.

"What I was trying to do was to get everyone to take two,which is most efficient... but some people wouldn'tlisten, like Green." [S4]

In all but one case, strategic influence occurred mostduring the middle and late trials of the simulation,suggesting that stopping the pool's decline was animportant motive underlying this action strategy. Thisalso suggests that strategic influence was employedprimarily as a reactive strategy in response to unfavor-able feedback.

Interestingly, participants did not employ other non-modelling strategies in their attempts to elicit coopera-tion from others. Axelrod (1984) has shown that tit-for-tat (i.e., cooperating when others cooperate and defectingwhen others defect) is an effective strategy for elicitingcooperation in two-person prisoner's dilemma games,and Wilke and Braspenning (1989) present evidence thatsome players may attempt to employ this strategy in N-person games. In the present study, however, fewparticipants responded to Green's repeated defection bydefecting themselves, and in the few instances in whichthey did, we found nothing in the protocols to suggestthat they were attempting to elicit cooperation fromGreen. Perhaps individuals only apply tit-for-tat insituations in situations where defecting does not seri-ously jeopardize one's ability to acquire points in thefuture (e.g., in situations where the pool is not in imme-diate danger of being extinguished, or, as in Axelrod'sstudies, where a replenishable pool is not even em-ployed). A second possibility raised by one of ouranonymous reviewers is that the presence of a coopera-tive player (Blue) may have prevented tit-for-tat fromemerging. Had both Green and Blue been non-coopera-tive, participants may have been more willing to employthis strategy.

General DiscussionWhat do the results of the present study tell us about theexisting theories of cognitive mediation in commonsdilemma theories?

LIMITED PROCESSING THEORYThe results support Dawes' (1980) general contentionthat individuals possess limited abilities to processinformation accurately, efficiently, and completely.Participants' protocols were replete with examples ofcognitive errors, confusion, and superficial processing.Also consistent with the limited processing view was thefinding that these factors tended to be associated withincreased rates of defection. Defection rates were muchhigher among confused participants than among lessconfused participants, and participants who thoughtmore about the implications of their choices were lesslikely to defect than those who thought less about theirchoices.

Cognitive Mediation in Commons Dilemmas 191

However, this last effect appears to be moderated byharvesters' goals. Simulating possible outcomes waslinked to restraint for participants with pool-preservationgoals, but not for participants who were unconcernedwith preserving the pool. This has important implica-tions for the limited processing view because it suggeststhat simply encouraging harvesters to think harder abouttheir choices will not necessarily increase restraint. Inorder for such an intervention to be effective, harvestersmust first be convinced that preserving the resource is aworthwhile goal. In short, deeper processing, by itself, isnot the panacea that will save the commons; a desire tocooperate must also be present.

GOAL-EXPECTATION THEORYAccording to goal-expectation theory (Pruitt & Kimmel,1977), two conditions must be satisfied for cooperation tooccur in mixed-motive dilemmas: participants mustrecognize that their long-term goals are better served bycooperating than defecting, and they must expect othersin their group to cooperate. The present study providesonly limited support for this theory. Most participantsreduced their harvests as the pool declined, suggestingthat they recognized (either implicitly or explicitly) thatmutual cooperation in this context was desirable. Expec-tancies that others would cooperate, however, did notappear to be a necessary prerequisite for cooperation.Many participants cooperated on the initial trial of thesimulation before they had developed expectancies abouthow others would choose. Furthermore, as the pool decli-ned, many reduced their harvests even when they expec-ted Green to defect. This is not to say that expectanciesabout others are not an important determinant of harvestchoice. The results from both this study and from previ-ous studies suggest that they probably do exert an influ-ence. Our point is that an expectancy that others willcooperate is not absolutely necessary for cooperation tooccur.

THREE-FACTOR MODEL OF HARVEST DECISIONSAccording to Samuelson and his colleagues (Messick etal, 1983; Samuelson & Messick, 1986a, 1986b; Samuelsonet al.; 1984), harvest decisions are governed by three mainmotives: a desire to use the resource wisely, self-interest,and a desire to conform to implicit group norms. In thepresent study, most participants reduced their harvestsas the pool declined, hoping that they could acquire morepoints in the long run by preserving the pool. This isconsistent with Samuelson et al.'s contention that thedesire to use pool wisely and self-interest are importantmotives.5 Furthermore, our finding that several partici-

5 The relative importance of the conformity motive could not beassessed in this study because Blue and Green were pre-program-med to adopt very different harvest strategies. Thus, pressure toconform to an implicit group norm was low throughout thesimulation.

pants increased their harvests late in the simulationwhen pool extinction appeared to be imminent suggeststhat motives may shift as the simulation progresses. Thisnotion of shifting motive is also consistent with recentfindings reported by Conley and Gifford (1996).

AN INTEGRATED PERSPECTIVEThe three theories discussed above tend to emphasizedifferent aspects of the decision-making process, and areperhaps best thought of as complementary, as opposedto competing, accounts of how harvest decisions aremade. The results of the present study provide a basis fordrawing together these diverse accounts into a broaderintegrated perspective.

The present analysis suggests that decision makingin commons dilemmas can be broken down into twomain components: a goal-formulation component anda strategy-implementation component. Harvesterstypically develop or adopt one or more harvest goals,and (hen implement specific strategies to achieve thosegoals. According to this perspective, defection canoccur for several reasons:1. Harvesters may fail to recognize the potential

benefits of preserving the resource pool. In thepresent study, most participants who cooperatedbelieved that their point-maximization goals wouldbe best served by restraining their harvests andpreventing the pool from being prematurely extin-guished. In several instances, however, limitedprocessing appears to have prevented or delayedparticipants from making this important association.If harvesters fail to recognize the advantages ofpreserving the pool, they are unlikely to restraintheir harvests. This is similar to the tenet of goal-expectation theory that participants must firstrecognize the advantages of mutual cooperationbefore adopting cooperation as a goal.

2. Harvesters may adopt other goals, such as acquiringmore points than others, that are incongruous withcooperation. Limited processing may or may notplay an important role in this decision. In someinstances, adopting a competitive goal may preventa participant from thinking about alternative coursesof action. In other cases, participants may be fullycognizant of the advantages of mutual cooperationand preserving the resource pool, but neverthelessdecide to pursue other goals. In terms of three-factortheory, this would be equivalent to a competitiveself-interest motive simply overpowering aresponsible-pool-use motive.

3. Harvesters may believe that even if they restraintheir harvests, others will continue to overharvestand extinguish the pool. This is similar to goal-expectation theory's tenet that participants will onlycooperate if they expect others to also cooperate, butwith an important difference. The present analysissuggests full-blown expectancies are not necessary

192 Hine and Gifford

for cooperation to occur. As noted previously, mostparticipants were willing to restrain their harvests aslong as they believed there was a chance that otherswould cooperate before the pool was extinguished.Participants increased their harvests only afterbecoming convinced that a mutually cooperativesolution was unlikely to be reached.

4. Harvesters may adopt cooperative goals, but fail toimplement effective strategies to achieve these goals.Cooperative goals do not guarantee cooperativebehaviour. The present study suggests that misap-plied cognitive or behavioural strategies (e.g., failingto monitor pool size accurately, making computa-tional errors, not developing an effective resource-management plan, failing to carefully consider theimplications of one's choices, etc.) can undermineeven the most cooperative intentions. Limitedprocessing may play an important role at this stageof the decision making process. If harvesters areunable or unwilling to allocate the cognitive re-sources needed to implement effective strategies, thepossibility that they will inadvertently overharvestfrom the pool will increase.

In terms of developing effective interventions, thepresent framework suggests that two separate aspects ofthe decision-making process must be targeted. Initially,steps must be taken to increase the likelihood thatharvesters will formulate and adopt cooperative harvestgoals. This may involve educating harvesters about thebenefits of cooperation and the dangers of competition,and convincing them that mutually cooperative solutionsare a realistic possibility. However, instilling cooperativegoals is not enough. Steps also must be taken to ensurethat harvesters are able to develop and implementeffective action strategies to achieve these goals. It shouldbe emphasized that these action strategies include, butare not limited to, the psychological processes high-lighted in this article. For example, Ostrom (1990) hasprovided a comprehensive list of collective institutionalstrategies associated with the successful long-termmanagement of common pool resources in the realworld.

LIMITATIONS AND FUTURE RESEARCHThis study employed an innovative methodology togenerate a dynamic, process-oriented account of thedecision making in commons dilemmas, something thatat present is lacking in the literature. This approachcontributed to the discovery of several important newfindings that would almost certainly have been over-looked had more traditional methods been employed,and provided the basis for developing a new integratedtheoretical perspective. Nevertheless, given the study'ssmall, unrepresentative sample and its unstructureddesign, many of our results should be considered to be

tentative and in need of further investigation.Researchers may wish to address the following issues

in future studies. Given the provisional nature of thecurrent findings, a replication is required involving alarger more representative sample of harvesters. Further-more, in several instances, design limitations preventedus from choosing between alternative explanations forobserved findings. Future studies should build incontrols to ensure that alternative explanations are ruledout.

Studies are also needed to determine whether and/orhow harvest goals and action strategies vary as a func-tion of harvester dispositions such as social values andrisk-taking propensity. Research should also explore theimpact of situational variables on the formation andimplementation of harvest goals and strategies. Forexample, how do goals and strategies differ whenharvest choices are anonymous opposed to public, whencommunication among group members is allowed asopposed to not allowed, when group sizes are small asopposed to large, when feedback suggests that theresource is being efficiently managed as opposed torapidly depleted?

To date, much of the psychological research oncommons dilemmas has been conducted in the labora-tory using computer simulations. Field research isneeded to determine whether the basic decision pro-cesses guiding behaviour in the laboratory generalize toother settings. Interviews with government and industryofficials, as well as other resource consumers, about theirdecision strategies may prove beneficial. The feasibilityof using think-aloud procedures and naturalistic obser-vation to track resource-use decisions as they unfold inthe real world should also be explored.

The authors wish to thank Elizabeth Brimacombe, DavidGartrell, Michael Hunter, Lome Rosenblood, CharlesSamuelson, and three anonymous reviewers for theirvery helpful comments on earlier drafts of this manu-script. We would also like to acknowledge the efforts ofTara Bookman, who assisted with the coding of the ver-bal protocols and interview transcripts.This research was supported by a SSHRC doctoral fellow-ship awarded to the first author.

Correspondence concerning this article should beaddressed to Donald Hine, Department of Psychology,University of New England, Armidale, NSW, 2351, Aus-tralia.

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Receive July 3,1996 - Revised November 20,1996Accepted April 17,1997