How to build a behavioural economics course: a complete guide for lecturers

Central question: What does behavioural economics add to the standard economic model of choice, and what counts as evidence that the benchmark needs to be extended?

Begin with the benchmark rather than with a list of biases. Students need a clean account of preferences, constraints, utility, optimisation and revealed choice before they can judge when bounded rationality, limited information or context-sensitive preferences improve explanation. The teaching aim is comparison, not caricature: standard models remain useful, while behavioural models make different assumptions when systematic patterns demand them.

What this concept covers

• Rational-choice benchmark and revealed preference
• Bounded rationality, satisficing and limited cognition
• Positive versus normative claims
• Laboratory, field and natural experiments
• Internal validity, external validity and replication

Learning outcomes

• State the standard choice benchmark precisely enough to test it
• Distinguish a modelling assumption from an empirical regularity
• Evaluate whether evidence supports a behavioural extension rather than a one-off anomaly

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Run a short in-class choice task before naming any theory, then compare observed choices with the benchmark.
• Use one result that fits standard theory and one that challenges it so students do not learn that behavioural economics means irrationality.
• Ask groups to identify what evidence would falsify their preferred explanation.

Case-style example

Northstar Benefits offers employees two pension-enrolment screens. In Screen A, 38 of 100 employees enrol; in Screen B, with identical funds and fees but a pre-selected contribution box, 67 enrol. Students calculate the 29-point difference, then write two rival explanations: a stable preference story and a bounded-rationality or default story. The task is to specify one additional test that would distinguish the explanations. Students discover that a behavioural claim needs an identification strategy, not just a surprising percentage.

Where students struggle

Students often treat the rational benchmark as obviously false or behavioural evidence as automatically superior. Push them to ask what each model predicts, which assumptions drive the prediction and whether the evidence distinguishes between competing mechanisms.

Reading and a quick check

Use Angner Chapters 1-3 and Thaler (2018) as an orientation. Quick check: ask students to write one sentence that separates a descriptive claim about behaviour from a welfare judgement about what people should choose.

Where it leads

The benchmark makes the next move disciplined: students can now examine recurring judgement shortcuts and ask when they help, when they mislead and how context changes the answer.

Central question: When do fast rules of thumb improve judgement, and when do anchoring, availability, representativeness or overconfidence create predictable error?

Teach heuristics as adaptive tools before teaching them as mistakes. Behavioural economics is strongest when students can identify a mechanism and predict when it should matter. Anchoring, base-rate neglect, availability, representativeness, overprecision and confirmation processes give a practical set of mechanisms for analysing managerial and consumer judgement under uncertainty.

What this concept covers

• Anchoring and insufficient adjustment
• Availability and salience
• Representativeness and base-rate neglect
• Overconfidence and overprecision
• Confirmation, motivated reasoning and selective search

Learning outcomes

• Diagnose a plausible judgement mechanism from evidence
• Separate heuristics that economise on cognition from biases that systematically distort decisions
• Design a simple test that could detect anchoring or overconfidence

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Start with two numerically different anchors in parallel groups and reveal the distributions.
• Have students predict the direction of a bias before discussing the evidence.
• Use calibration questions so students compare confidence intervals with actual accuracy.

Case-style example

Cedar Analytics asks two sales teams to forecast a new product. Team A first sees an internal target of £8.0m; Team B sees a conservative benchmark of £4.5m. After identical market data, median forecasts are £7.2m and £5.6m. Students compute the forecast gap, identify at least two possible mechanisms, then design a debiasing protocol for the CFO. They discover that removing the anchor is not enough if incentives, selective evidence search or overconfidence still push estimates in the same direction.

Where students struggle

The common failure is naming a bias after the fact. Require an ex ante prediction: if anchoring is the mechanism, what should happen when the anchor changes, when expertise rises, or when independent estimates are collected first?

Reading and a quick check

Pair Angner Chapters 4-5 with Gigerenzer, Reb and Luan (2022). Quick check: give a short scenario and ask for the mechanism, predicted direction and one alternative explanation.

Where it leads

Students are ready to move from judgement to choice, where outcomes are evaluated relative to reference points and losses can carry different psychological weight from gains.

Central question: Why can the same final wealth position produce different choices depending on the reference point, framing and whether outcomes are coded as gains or losses?

Prospect theory is a core bridge from classic behavioural evidence to applied decision-making. Teach the value function, reference dependence, diminishing sensitivity and probability weighting conceptually, then use simple numerical choices. The objective is not to turn the class into a curve-fitting exercise but to make students reason about which reference point is psychologically active and what changes when it shifts.

What this concept covers

• Reference points and coding of gains and losses
• Diminishing sensitivity
• Loss aversion and the endowment effect
• Framing and attribute evaluation
• Probability weighting and certainty effects

Learning outcomes

• Use prospect-theory logic to predict changes in choice
• Identify plausible reference points in a market or organisational setting
• Distinguish loss aversion from risk aversion and from simple status-quo preference

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Use paired gain-frame and loss-frame problems with identical expected values.
• Ask students to debate the relevant reference point before calculating anything.
• Connect the theory to price promotions, compensation, investment decisions and policy communication.

Case-style example

Harbour Mobile sells a £600 handset. Option 1 is framed as “£60 cashback after purchase”; Option 2 is “£540 price today, but a £60 fee if returned within 14 days.” In a pilot, 54% choose Option 1 while 72% keep Option 2 after purchase. Students map the reference points, compute the economic equivalence, then predict which frame will create greater perceived loss at return. They discover that identical final payoffs can generate different behaviour when ownership and reference points change.

Where students struggle

Students often equate loss aversion with any dislike of risk. Make them state the reference point and show what would count as a loss. Also distinguish evidence for a pattern from a universal claim that every individual is loss averse in every context.

Reading and a quick check

Use Angner Chapters 6-7 and Bleichrodt and L'Haridon (2023) on the robustness of loss aversion to stake size. Quick check: ask students to rewrite one decision problem with a different reference point and predict the direction of change.

Where it leads

Prospect theory introduces non-linear responses to risk. The next concept deepens that analysis by separating known probabilities, ambiguity, experience and subjective probability judgement.

Central question: How should lecturers teach the difference between risky choice, ambiguous choice and biased probability judgement without collapsing them into one idea?

Students need to distinguish preferences over outcomes from beliefs about probabilities. Expected utility provides the risk benchmark; behavioural work adds probability weighting, ambiguity attitudes, description-experience gaps and systematic errors in probability judgement. This distinction becomes important in finance, insurance, entrepreneurship, health and strategic decision-making.

What this concept covers

• Risk versus uncertainty and ambiguity
• Expected utility benchmark
• Probability weighting and rare events
• Ambiguity attitudes
• Description versus experience and learning from samples

Learning outcomes

• Separate probability beliefs from utility over outcomes
• Compare expected-utility and behavioural predictions in simple lotteries
• Evaluate when experience-based choices may diverge from description-based choices

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Use a two-lottery task, then change only whether probabilities are described or learned from repeated draws.
• Ask students to estimate a probability first, then make the choice, so belief and preference can be discussed separately.
• Bring in insurance, cybersecurity and startup failure probabilities as applications.

Case-style example

Aster Cyber can buy Protection A for £40,000 against a 1% annual breach loss of £3m, or Protection B for £85,000 against an uncertain breach probability estimated somewhere between 0.5% and 3%. Students calculate expected losses at the interval endpoints, then compare decisions under risk neutrality, risk aversion and ambiguity aversion. Two teams can defensibly choose differently. Students discover that disagreement may come from beliefs about probabilities as much as from attitudes toward outcomes.

Where students struggle

Students regularly say “risk aversion” when the real issue is ambiguity or a probability estimate. Mark separately whether they have modelled the belief, the payoff and the preference.

Reading and a quick check

Use Angner Chapters 4-7 and Hertwig and Wulff (2022). Quick check: give one described lottery and one experience-based task and ask which component of the decision environment changed.

Where it leads

Risk decisions compare uncertain outcomes today. Intertemporal choice asks how people trade rewards across time, including when preferences change as the decision approaches.

Central question: Why do people plan to save, study, exercise or invest tomorrow but reverse the plan when tomorrow becomes today?

The discounted-utility benchmark gives students a clean reference model for choices across time. Behavioural economics then introduces present bias, quasi-hyperbolic discounting, temptation, commitment devices and projection errors. The strongest teaching examples ask students to distinguish high impatience from dynamic inconsistency.

What this concept covers

• Exponential discounting benchmark
• Present bias and quasi-hyperbolic preferences
• Dynamic inconsistency
• Commitment devices and self-control
• Procrastination, deadlines and temptation

Learning outcomes

• Distinguish impatience from present-biased preference reversal
• Explain why a commitment device can be welfare-improving for a sophisticated present-biased agent
• Design a simple intervention that targets timing rather than information

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Ask students to choose between £100 today and £115 next month, then between £100 in 12 months and £115 in 13 months.
• Use a deadline-setting exercise that distinguishes naive and sophisticated procrastinators.
• Connect to savings defaults, subscription cancellation, study planning and employee benefits.

Case-style example

Rivermark Consulting gives analysts a compliance module due in 30 days. With one final deadline, 62% complete in the last 48 hours and 14% miss it. A second cohort receives self-chosen intermediate deadlines; 79% finish before the final week but 9% miss a self-set milestone. Students compare the patterns, identify present-bias and commitment-device predictions, then recommend a deadline architecture. They discover that more commitment can reduce procrastination but also creates rigidity and failure costs.

Where students struggle

Students often infer present bias from any late action. Ask whether a stable high discount rate, uncertainty, workload shocks or strategic delay could produce the same pattern.

Reading and a quick check

Use Angner Chapters 8-9. Quick check: ask students whether a preference reversal is required to diagnose present bias and what data they would need to observe one.

Where it leads

Time inconsistency shows that choice can depend on how resources are mentally represented. Mental accounting and framing make that representation explicit.

Central question: How do labels, ownership and prior expenditure change decisions even when standard economic opportunity costs are unchanged?

Mental accounting is highly teachable because students can see how budgets, labels and transaction histories affect behaviour. Link it to the endowment effect, sunk-cost reasoning, payment decoupling and transaction utility. This concept works best when lecturers make students identify the normative benchmark before diagnosing the behavioural departure.

What this concept covers

• Mental budgets and account labels
• Transaction utility and perceived deals
• Endowment and ownership effects
• Sunk costs and escalation of commitment
• Payment salience and decoupling

Learning outcomes

• Identify when account labels change consumption or spending
• Apply opportunity-cost logic to a sunk-cost decision
• Evaluate whether a pricing or product design uses framing without changing underlying economic value

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Use paired scenarios with identical future payoffs but different historical costs.
• Run a willingness-to-pay versus willingness-to-accept exercise using a low-value object.
• Ask students to redesign a subscription or expense policy to reduce harmful mental accounting.

Case-style example

Orbit Events has spent £240,000 developing a conference platform. A new forecast shows that launching requires another £90,000 and will generate only £65,000 of incremental contribution. A replacement platform costs £70,000 and generates £95,000. Students calculate the forward-looking payoff from each option, then write the manager’s likely sunk-cost argument and a corrective decision memo. Students discover why psychologically “wasting” past expenditure can feel worse than accepting a smaller future payoff.

Where students struggle

Students can name the sunk-cost fallacy but still use sunk costs rhetorically. Insist on a forward-looking comparison and then discuss why identity, accountability or regret can make the past psychologically active.

Reading and a quick check

Use Angner Chapters 2-3 and supplementary textbook sections on framing and ownership. Quick check: ask students to identify the opportunity cost in a sunk-cost scenario before discussing psychology.

Where it leads

So far the unit of analysis has mainly been the individual. Social preferences broaden the model to fairness, reciprocity, norms and concern for others.

Central question: When do people sacrifice material payoff for fairness, reciprocity, identity or social norms, and how should economists model those motives?

Social preferences let students see how behaviour can be systematically other-regarding without abandoning economic modelling. Dictator, ultimatum, trust and public-goods games provide clean teaching structures. The advanced move is to distinguish distributional preferences from reciprocity, beliefs about others and norm compliance.

What this concept covers

• Altruism and distributional preferences
• Inequality aversion
• Reciprocity and trust
• Social norms and conditional cooperation
• Identity, merit, luck and perceived fairness

Learning outcomes

• Predict how social preferences can alter equilibrium behaviour
• Distinguish fairness concerns from strategic reputation or repeated-game incentives
• Interpret experimental evidence on heterogeneity in social preferences

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Run a low-stakes ultimatum or public-goods exercise and graph the class distribution rather than just the mean.
• Change whether unequal outcomes arise from merit or luck.
• Ask students to explain whether a fairness claim is about outcomes, procedures or intentions.

Case-style example

Mosaic Logistics allocates a £120,000 team bonus. Plan A gives £60,000 to the top performer and £12,000 to each of five colleagues. Plan B gives £30,000 to the top performer and £18,000 to each colleague. Productivity data show the top performer generated 42% of measured value, but a system outage outside colleagues’ control hurt their output. Students compare equality, merit and reciprocity arguments, then predict acceptance under each plan. They discover that perceived process fairness can matter independently of final payoffs.

Where students struggle

Students often call any cooperative action altruistic. Ask what a purely strategic self-interested agent would do and what additional evidence is needed to infer social preference rather than reputation or repeated interaction.

Reading and a quick check

Assign Fehr and Charness (2025) on social preferences. Quick check: ask students to give one observation that would distinguish reciprocity from inequality aversion.

Where a simulation fits

The Corporate Governance simulation can follow social-preference teaching. The role-based conflict gives a setting for analysing fairness, motivated reasoning, role incentives, investor confidence and how stakeholder perspectives affect what counts as an acceptable resolution.

Where it leads

Social motives become strategically important when decision-makers interact. Behavioural game theory shows how beliefs, limited reasoning and fairness alter bargaining and coordination.

Central question: How do limited strategic depth, social preferences, focal points and negotiation behaviour change predictions from classical game theory?

Use analytical game theory as the benchmark and then introduce level-k reasoning, beauty-contest evidence, bargaining, coordination and psychological motives. The objective is to help students explain why observed play may converge toward equilibrium in some settings but systematically depart from it in others.

What this concept covers

• Nash equilibrium as a benchmark
• Level-k and cognitive-hierarchy reasoning
• Ultimatum and bargaining behaviour
• Coordination, focal points and conventions
• Learning, repeated play and belief updating

Learning outcomes

• Solve a simple benchmark equilibrium before applying a behavioural model
• Explain how beliefs about others’ reasoning change optimal action
• Evaluate bargaining outcomes using both economic terms and behavioural mechanisms

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Run a beauty-contest game twice to show learning and strategic depth.
• Use a bargaining exercise with asymmetric information and role-specific objectives.
• Debrief both outcome and process: initial anchors, concessions, perceived fairness and belief updating.

Case-style example

Valeo Ventures and founder Maya negotiate a seed investment. The founder opens at a £6m pre-money valuation; the investor’s internal target is £4m. Maya values one board seat at £300,000 of equivalent valuation, while the investor values veto rights at £450,000. Students construct at least two packages with similar total economic value, then predict which will be accepted under anchoring, reciprocity and loss-framing. Students discover that negotiation is about traded dimensions, beliefs and reference points, not just splitting a fixed pie.

Where students struggle

Students sometimes use behavioural explanations as an excuse not to solve the benchmark game. Require the equilibrium first, then ask what observed behaviour the behavioural model explains better.

Reading and a quick check

Use Angner Chapters 10-11. Quick check: ask students to predict first-round and later-round behaviour in a beauty-contest game and explain why the predictions differ.

Where a simulation fits

The Startup Funding simulation can follow behavioural game theory. Startup and VC teams negotiate a complete term sheet, giving lecturers a concrete setting for anchors, reference points, strategic beliefs, concession behaviour and the difference between headline valuation and the complete package of ownership and control.

Where it leads

Strategic interaction explains how other people shape choice. Attention and choice architecture then show how the structure of the decision environment itself can shape what people notice and select.

Central question: How can what is noticed, ordered, pre-selected or made easy alter choice without changing the formal set of options?

Choice architecture is more rigorous when students can specify a mechanism. Defaults may exploit inertia, imply endorsement or reduce action costs. Salience can redirect attention. Larger menus can help or hinder depending on search costs and expertise. The teaching goal is to move beyond “nudges work” toward mechanism, heterogeneity and boundary conditions.

What this concept covers

• Limited attention and salience
• Defaults and status-quo effects
• Choice overload and search costs
• Ordering, partitioning and attribute prominence
• Sludge, friction and ease

Learning outcomes

• Diagnose how a choice architecture changes attention or action costs
• Predict when a default is likely to have a larger or smaller effect
• Design an architecture that preserves meaningful choice while reducing avoidable friction

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Give groups the same options in different orders or with different defaults.
• Ask students to identify whether the intervention changes information, incentives, attention or effort.
• Require a heterogeneity forecast: which users are most likely to be helped or harmed?

Case-style example

ClearBank offers four savings plans. In the original screen, users choose from all four and 41% select the lowest-yield cash option. In a redesigned screen, a diversified plan is pre-selected, fees are shown first and users must actively opt out; 64% retain the default. Students calculate the 23-point shift, identify endorsement, inertia and attention mechanisms, then design a test that separates them. Students discover that the same behavioural outcome can arise from different mechanisms with different welfare implications.

Where students struggle

Students can treat every interface change as a nudge. Ask which choice-set elements are unchanged, what friction changed and how welfare will be assessed for users with different preferences.

Reading and a quick check

Use Angner Chapter 12 with Beshears and Kosowsky (2020) as background if the lecturer wants an additional review. Quick check: ask students to classify an intervention as information, incentive, default, simplification or friction change.

Where it leads

These mechanisms travel directly into markets. Consumer choice, pricing and product design show how behavioural responses become commercially consequential.

Central question: How do reference prices, decoys, partitioned prices, social proof and segmentation interact with demand and competitive response?

This concept brings behavioural economics into a business-school setting without turning the module into consumer psychology. Keep price and demand logic visible, then show where context-dependent preferences and heterogeneous response affect segmentation, positioning and commercial design. The strongest classes require students to predict both customer response and competitor adaptation.

What this concept covers

• Reference prices and price endings
• Decoy and compromise effects
• Partitioned prices and payment salience
• Social proof and scarcity cues
• Heterogeneous response, segmentation and competitive adaptation

Learning outcomes

• Connect behavioural mechanisms to demand and positioning decisions
• Evaluate whether a pricing frame changes perceived value without changing total price
• Predict how competitor response can erode a behavioural advantage

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Use a three-option decoy exercise, then remove the decoy and compare shares.
• Give students segment data and ask which behavioural mechanism is most plausible for each segment.
• Require an ethics check whenever design intentionally exploits inattention or confusion.

Case-style example

Vela Coffee tests three subscription menus. Menu A offers Basic £18 and Premium £28, producing a 62/38 split. Menu B adds a £27 “Plus” plan with fewer Premium features; Premium share rises to 57%. Menu C keeps three plans but shows annual spend in large type, reducing Premium to 45%. Students calculate the share changes, identify decoy and payment-salience mechanisms, then recommend a launch menu and an ethical disclosure rule. They discover that choice context can shift demand even when core product quality is unchanged.

Where students struggle

Students often stop at “customers are biased.” Make them link a mechanism to a measurable demand response and explain how a rational or learning-based alternative could produce similar data.

Reading and a quick check

Use Cartwright for applied market examples and Thaler (2018) for broader context. Quick check: ask students to separate a price effect, a framing effect and a segmentation effect in one commercial example.

Where a simulation fits

Use the Go To Market simulation after students have studied consumer choice and pricing. The simulation does not teach behavioural-economics theory directly; the lecturer can use the debrief to examine segmentation heuristics, pricing anchors, evidence use, salience and the coherence of choices across customer segments.

Where it leads

Commercial applications create a natural bridge to deliberate intervention design. The next concept asks how to build, test and scale behavioural interventions rather than merely describe them.

Central question: How should students design, test and scale behavioural interventions while taking effect size, context and publication bias seriously?

This concept should combine choice architecture with experimental method. Teach intervention logic, treatment arms, outcomes, randomisation, power, heterogeneity and scaling. Pair academic meta-analysis with large-scale nudge-unit evidence so students see why a promising mechanism can produce modest effects in practice.

What this concept covers

• Nudges, boosts and behavioural public policy
• Randomised controlled trials and A/B tests
• Outcome measures, treatment effects and practical significance
• Heterogeneous treatment effects and targeting
• Scale, publication bias, persistence and implementation

Learning outcomes

• Design a credible behavioural experiment with a clear outcome and comparison group
• Interpret effect size separately from statistical significance
• Evaluate whether evidence supports scaling, redesign or abandonment

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Give students a policy or business problem and require a theory-of-change sentence before any intervention idea.
• Ask for a pre-specified primary outcome and one guardrail metric.
• Compare an academic effect size with a scaled operational result and debate why they differ.

Case-style example

MetroEnergy wants more customers to switch to paperless billing. Baseline uptake is 46%. A pilot default increases uptake to 58% among 4,000 customers, while a social-norm message reaches 51%. Complaints rise from 1.1% to 2.4% under the default. Students calculate treatment effects and complaint changes, then recommend scale, redesign or rejection using both uptake and welfare guardrails. They discover that the largest behavioural effect is not automatically the best intervention once friction, heterogeneity and welfare are considered.

Where students struggle

Students can confuse statistical significance with practical value and may ignore counter-metrics. Require effect sizes, baseline rates, welfare criteria and a scaling hypothesis.

Reading and a quick check

Pair DellaVigna and Linos (2022) with Mertens et al. (2022). Quick check: ask students which result would make them stop a nudge even if the primary outcome improved.

Where it leads

The final concept makes explicit what good behavioural-economics teaching must retain: welfare, ethics, evidence quality, heterogeneity and the limits of intervention.

Central question: When is it legitimate to infer that a behavioural intervention improves welfare, and how should students respond to contested evidence or heterogeneous effects?

Close the course by resisting a triumphalist narrative. Behavioural economics contains competing models, replication debates and normative disagreements. Welfare can be difficult when observed choices are context-sensitive. Ethical evaluation should ask about autonomy, transparency, distributive effects, manipulation and who gets to define the target behaviour.

What this concept covers

• Revealed preference and welfare under inconsistent choice
• Libertarian paternalism, autonomy and manipulation
• Boosting versus nudging
• Replication, publication bias and effect heterogeneity
• Cultural portability, distributional effects and practitioner ethics

Learning outcomes

• Critique a behavioural claim using evidence quality and boundary conditions
• Evaluate an intervention using explicit welfare and ethical criteria
• Defend when a behavioural intervention should be scaled, modified or rejected

Supports the course outcomes on analysis, evaluation and defensible application.

How to teach it

• Use a structured debate in which one team defends a nudge and another defends a boost, regulation or no intervention.
• Ask students to identify whose welfare standard is being used.
• Finish with a replication audit of one famous effect and require a decision about what should still be taught as robust.

Case-style example

CivicPay redesigns tax reminders. A loss-framed message raises on-time payment from 71% to 76%, but a follow-up survey finds 18% of recipients felt unduly threatened versus 7% under the neutral message. Effects are concentrated among low-income filers. Students calculate the overall and subgroup changes, then write a 250-word ethics and welfare recommendation. Two defensible conclusions are possible. Students discover that behavioural effectiveness, distributional impact and legitimacy are separate dimensions of the decision.

Where students struggle

Students can use “ethics” as an unstructured opinion section. Require criteria: autonomy, transparency, proportionality, distribution, reversibility, evidence strength and plausible alternatives.

Reading and a quick check

Use Herzog and Hertwig (2025) on boosting and a recent course discussion of nudge evidence. Quick check: ask students to state the welfare criterion they would use before judging whether an intervention is successful.

Where it leads

This closes the lifecycle. Students should now be able to move from benchmark theory through behavioural mechanism and evidence to an applied recommendation that can survive methodological and ethical challenge.

10-week course structure

A 10-week version should combine related mechanisms rather than dropping risk, social preferences or ethics. Keep the benchmark at the start and the intervention-evaluation logic at the end.

14-week course structure

A 14-week course creates room for experimental method, a longer negotiation simulation, richer evidence appraisal and separate capstone presentations.

The Go To Market simulation is the closest commercial application in the current Finsimco portfolio. Students work individually and competitively through a defined product launch, using customer needs, buying behaviour and evidence to make segmentation, targeting and positioning decisions. The behavioural-economics value comes from the lecturer's framing and debrief: the simulation provides observable choices under limited information, but it is not a direct test of prospect theory, heuristics or bounded rationality.

Why it fits here

Place it after consumer choice, pricing and attention. Students already know how reference points, salience, limited attention and heterogeneous preferences can influence market response, so they can critique not only whether a strategy scored well but why a decision was made and what evidence it relied on.

What students do

• Review the company mission, product and US market context in a short briefing.
• Analyse customer motivations, benefits sought and buying behaviour to segment the market.
• Choose one primary target segment, two launch regions, a targeting approach and an access channel.
• Set a positioning theme, frame of reference, point of difference, value proposition, price, pack format and promotional message.
• Review score, rank and class patterns in the Reflection stage, then explain where salience, anchoring, heuristics or evidence quality may have influenced their own choices.

Recommended course placement

• After Session 10, once consumer choice, pricing and choice architecture have been taught.
• Inside a two-hour class, followed by a 20-30 minute behavioural debrief.
• Before the capstone experiment design, so students can turn an observed commercial pattern into a testable hypothesis.

Knowledge students should have before the simulation

• Segmentation, targeting and positioning at a basic level.
• Reference prices, framing, salience and limited attention.
• The distinction between a behavioural mechanism and a post-hoc label.
• How to use market evidence without assuming one metric determines the answer.

Delivery format

Suggested debrief questions

• Which information did you notice first, and what did you ignore?
• Did a price or segment benchmark act as an anchor?
• Which choice was most sensitive to framing or salience?
• Where did you rely on a heuristic because the evidence was incomplete?
• What experiment would distinguish your behavioural explanation from a standard demand or information explanation?
• Would the highest-scoring strategy also be the best choice for every customer segment?

The Startup Funding simulation places students on both sides of an early-stage seed-funding negotiation. Startup and VC teams independently analyse the company and draft opening term sheets before seeing the other side's position, then negotiate funding amount, valuation, ownership, dilution, option pool, board representation, consent rights and investor protections. For Behavioural Economics, the value is the observable bargaining process: anchors, reference points, concessions, beliefs and perceived fairness become discussable rather than hypothetical.

Why it fits here

Place it after social preferences and behavioural game theory. Students can solve a benchmark bargaining problem first, then examine why actual negotiations diverge because of anchoring, role objectives, fairness, limited strategic depth, loss framing and belief updating.

What students do

• Watch a 7-minute briefing and learn whether they represent the Startup or VC side.
• Spend about 90 minutes in independent analysis, reviewing the case and drafting an opening term sheet.
• See the counterparty opening terms, classify disagreements and decide which provisions are essential or tradable.
• Negotiate the complete agreement provision by provision during a time-pressured Structuring stage of up to 120 minutes.
• Review final terms and cohort comparisons in Reflection, including valuation, ownership, dilution, governance rights, investor protections and whether a deal completed.

Recommended course placement

• After Session 8, when bargaining, strategic reasoning and social preferences are already familiar.
• Across two classes or one longer workshop, with a separate behavioural-economics debrief.
• As evidence for an individual reflection that compares the team’s opening anchor with its final agreement.

Knowledge students should have before the simulation

• Basic bargaining and game-theory benchmark logic.
• Anchoring, reference dependence and loss framing.
• Social preferences, reciprocity and fairness.
• Basic seed-funding vocabulary; the simulation is beginner-friendly and defines the terms in its materials.

Delivery format

Suggested debrief questions

• What was your opening anchor, and how far did the final agreement move from it?
• Which concession felt like a loss even when it improved the complete package?
• How did beliefs about the other side change after opening terms were revealed?
• Where did fairness or reciprocity affect a decision that could not be explained by valuation alone?
• Did time pressure change strategic depth or cause teams to accept a focal compromise?
• What evidence would you need to claim that an observed bargaining pattern is a behavioural regularity rather than a one-off team dynamic?

Grading with the Go To Market Simulation

Students develop an individual segmentation, targeting and positioning strategy across a roughly 1.5-2 hour competitive activity. In a Behavioural Economics course, assess the analytical interpretation of their choices rather than treating the platform score as a direct measure of bias.

• Quality of mechanism diagnosis: which behavioural concepts plausibly influenced a choice and what evidence supports that claim.
• Use of market evidence and alternative explanations.
• Ability to connect pricing, salience or segment choice to a testable hypothesis.
• Individual reflection on what would be changed in a second decision round.

Grading with the Startup Funding Simulation

Startup and VC teams independently prepare opening positions and then negotiate a complete seed-round term sheet. Use the final agreement and process evidence to support judgement, then add individual work that makes the student’s own behavioural analysis attributable.

• Opening anchor versus final agreement and explanation of movement.
• Evidence of strategic belief updating and concession logic.
• Use of fairness, reciprocity, reference dependence or loss framing without overclaiming.
• Individual defence of one behavioural mechanism and one standard strategic alternative.

Grading with the Corporate Governance Simulation

Students prepare in CEO, CTO, CCO and Shareholder teams, then negotiate a resolution to a conflict-of-interest case. The platform records decisions and role-specific outcomes that can support debrief and assessment.

• Accuracy of stakeholder incentive and social-preference analysis.
• Use of evidence rather than motivated reasoning or role rhetoric alone.
• Quality of the negotiated resolution and future safeguards.
• Individual explanation of how role, fairness, beliefs and strategic interaction affected the final decision.

The platform records decisions, agreements and comparative outcomes. That evidence supports academic judgement; it does not replace it, and it does not by itself establish which individual student made which argument. Add an attributable individual component where the simulation contributes to summative assessment.