1. Introduction

Negotiation is a process of structured communication and strategic interaction. While economic models often depict negotiators as rational actors, real-world outcomes are shaped by cognitive biases that influence perception, judgment, and decision-making [1]. Two biases stand out in negotiation research: anchoring, where initial offers disproportionately affect the bargaining range, and framing, where the presentation of terms as gains or losses changes risk preferences. Recent evidence suggests that these biases often co-occur and may produce effects greater than the sum of their parts [2].

This paper examines the interplay between anchoring and framing in negotiation, then evaluates practical approaches for mitigating these cognitive biases. By focusing on recent research, the paper proposes an expanded framework for analyzing the two biases in negotiations combined effects and demonstrates its application through three detailed case studies. The analysis offers actionable recommendations for practitioners and identifies promising directions for future research.

2. Theoretical background

2.1. Anchoring bias in negotiation

Anchoring bias, first conceptualized by Tversky and Kahneman, remains a central finding in behavioral decision research and has substantial implications for negotiation. In essence, the bias occurs when an individual relies too heavily on an initial piece of information-the anchor-when making subsequent judgments, even if the anchor is arbitrary or irrelevant [3].

Within the context of negotiation, anchors often take the form of the first offer, which creates a Zone of Possible Agreement (ZOPA) focal point. Subsequent offers tend to gravitate toward this anchor through the process of insufficient adjustment [4]. Recent research from 2020–2025 has provided more nuanced insights into anchoring.

Some scholars demonstrated anchoring persistence even among experienced managers, when evaluating performance scores [5]. Their study found that a higher numerical suggestion at the outset skewed performance ratings upward, despite clear evaluation criteria.

Some scholars provided experimental evidence that activating a “choice mindset” reduced anchoring effects [6]. This nudge encouraged negotiators to consider multiple counteroffers and prevented overreliance on the first number provided.

Other scholars examined anchoring in multi-attribute decision-making frameworks, revealing that anchor values biased weight elicitation for attributes in both SMART and Swing decision-making methods, indicating that anchoring effects extend beyond direct monetary offers to underlying evaluation structures [7].

Anchoring has been shown to exert both economic and psychological consequences in negotiations: economically by pulling settlements toward the anchor, and psychologically by influencing perceived fairness and satisfaction with the outcome [8].

2.2. Framing bias in negotiation

Framing bias occurs when identical information leads to different choices depending on whether it is presented as a gain or a loss [9]. This is grounded in Prospect Theory, which predicts that individuals are risk-averse in the domain of gains but risk-seeking in the domain of losses.

While framing research has been prolific in decision sciences, negotiation-specific empirical framing studies have expanded in recent years. Some scholars proposed a formalized belief-revision model, showing how framing systematically alters the revision process of perceived facts, especially when integrated with anchoring effects [10]. Some scholars found that gain-framed proposals (“you secure an additional 5% market share”) were more persuasive in cooperative negotiations, whereas loss-framed proposals (“you avoid losing 5% market share”) induced more aggressive counteroffers in competitive contexts [11]. Some scholars experimentally showed that loss frames increased willingness to prolong negotiations, consistent with the risk-seeking pattern predicted by Prospect Theory [12]. Framing effects are particularly influential in high-stakes negotiations where uncertainty is high, such as mergers and acquisitions or diplomatic agreements, because the emotional salience of loss avoidance can outweigh rational cost–benefit calculations.

2.3. Interaction between anchoring and framing

Though traditionally studied in isolation, anchoring and framing often occur simultaneously in real-world negotiations. A first offer (anchoring) can be framed either as a gain or as loss avoidance, magnifying or moderating the anchoring effect. Recent literature suggests three interaction patterns. The first one is Additive Effect, which means– Both biases independently push in the same direction, amplifying movement toward the anchor. The second one is Countervailing Effect, which means one bias mitigates the other, such as a gain-framed anchor inducing conservatism that tempers an aggressive numeric anchor. The third one is Multiplicative Effect, which means the presence of both biases creates non-linear effects, as found in simulated negotiations, where loss-framed high anchors produced the most risk-seeking responses [11]. This combined-bias perspective is increasingly relevant as negotiators frequently encounter offers that are simultaneously numeric and rhetorically framed.

3. Case examples

3.1. Job-offer negotiation

A mid-career marketing manager is interviewing for a senior position at a technology firm. The average market salary for comparable roles in the region is estimated at $80,000. During the negotiation, the recruiter makes the first offer at $70,000, which serves as a low anchor. In one experimental condition, the recruiter presents the offer as a gain by stating that the salary is $10,000 higher than the industry’s lower quartile. In another condition, the same offer is framed as a loss avoidance by explaining that accepting it would prevent losing $10,000 compared to the company’s entry-level senior hires.

In the study design, half of the participants receive a choice mindset nudge before the negotiations begin. They are reminded that they have multiple possible responses to any offer and are encouraged to consider at least three alternatives before accepting or countering.

The results indicate that in the control group with gain framing, participants anchored close to the initial $70,000 offer and settled at an average of $73,000. In the control group with loss framing, participants made larger counteroffers, which led to an average settlement of $75,000. In the control group using loss framing, participants made higher counteroffers, ultimately reaching an average settlement of $75,000. When the control group used mindset nudging, negotiators achieved settlements of $76,000 on average.

The highest settlement occurred in the group that combined a choice mindset with a loss frame, where participants negotiated more actively, ultimately reaching an average settlement of $78,000.

The above controlled experiments show that even when anchoring is low, loss framing tends to increase negotiators’ motivation to propose a new quoted price and their resolve to counter-anchoring. This effect is further enhanced when a choice mindset is added to the negotiation framework, encouraging negotiators to make multiple counteroffers and increase the number of negotiations rounds in practice.

This pattern is consistent with the work of Savani and Wadhwa, who found that prompting negotiators to consider multiple options reduces negotiators’ reliance on anchors and stimulates more creative and strategic bargaining behavior [9].

3.2. Procurement contract negotiation

A regional hospital was preparing to purchase a batch of medical imaging equipment. They were negotiating with a medical equipment supplier. Historical data showed that contracts of equivalent size typically ranged from $450,000 to $470,000. In this negotiation, the supplier employed a high price anchoring strategy, initially quoting $500,000. In one scenario, the supplier explained the profit margin of this offer and told the procurement team that their anchored price offered 15% savings over the company's standard retail price. In another scenario, the same offer is framed as loss avoidance, with the supplier stating that by securing the deal immediately, the hospital would avoid a 15 percent cost increase forecast for the following quarter.

In half of the experimental negotiations, the procurement teams have access to an AI-powered decision-support tool. This system analyzes historical purchase records, identifies fair market price ranges, and recommends counter anchors, such as a range between $455,000 and $465,000.

The results show that in the control condition with gain framing, final agreements tend to cluster near $480,000, staying close to the supplier’s initial anchor. In the control conditions with loss framing, negotiators counter more assertively and settle at an average of $470,000. When the AI tool is combined with gain framing, the average settlement drops to $465,000. The most favorable results occur in the AI tool and loss frame combination, where procurement teams use the tool’s suggested counter anchors effectively and achieve an average settlement of $460,000.

These findings indicate that the AI tool helps to break the psychological pull of a high anchor by providing credible benchmarks and specific counteroffer suggestions. While loss framing without AI support does improve outcomes compared to gain framing, it is less effective than technology-based intervention. This outcome aligns with the findings of Tan et al., who emphasize the value of technology-mediated debiasing in negotiation contexts [10].

3.3. Diplomatic trade agreement simulation

Delegations from two trading nations are negotiating a bilateral trade agreement aimed at reducing tariffs in the agricultural import market. The current base tariff rate is 15%. One delegation began with an anchoring strategy, proposing a tariff reduction from 15% to 10%. Under the gain framework, this proposal was interpreted as providing market access for imported agricultural products worth $50 million annually. Under the loss framework, the same proposal was described as avoiding a potential $50 million loss of market competitiveness over the next year.

Both delegations completed pre-negotiation framing training. This training included role-playing exercises simulating real-world negotiation scenarios, allowing negotiators to practice identifying offers and reframing them into preconditions based on their own interests. The training also covered the strategic use of counter-anchoring, enabling participants to respond to the other party's initial proposal based on objective facts.

The results showed that under the gain framework, the negotiating parties reached a final agreement more quickly, ultimately adjusting tariffs to 10%-10.5%. Under the control conditions using a loss framework, negotiations took longer and ultimately resulted in a tariff rate of 11% to 11.5%, although this process led to increased tensions between the two sides.

When training was combined with a gain framework, both delegations successfully reframed their offers in a neutral manner, setting the anti-anchor rate at 12%, ultimately reaching an agreement at 11.5%. Furthermore, the mood and atmosphere between the two sides were relatively relaxed throughout the negotiation process.

When training was combined with a loss framework, the delegations neutralized the emotional pressure brought on by the loss framework and proposed an evidence-based anti-anchor rate at 12.5%, ultimately reaching an agreement at 11.7%.

These findings suggest that training designed to recognize and counter both anchors and frames improves not only the economic terms of an agreement but also the quality of the relationship between parties. The training also reduces the prolongation effect of loss framing, a dynamic documented in the work of Kirk et al [3].

4. Results synthesis

The anchoring effect is persistent. Across all cases, anchors exerted strong and measurable pull-on settlement values, regardless of context, negotiator expertise, or availability of objective market information. Even in the procurement scenario, where historical pricing data was accessible, the initial supplier bid still influenced final agreements

Framing effects were equally robust, though they operated differently depending on the domain. Loss frames reliably produced more aggressive counteroffers, consistent with Prospect Theory’s prediction of risk-seeking behavior in the domain of losses. In the job-offer case, this manifested as candidates pushing settlements $2K–$3K higher on average than in gain-framed conditions. Gain frames facilitated quicker agreements, but often at the cost of smaller deviations from the initial anchor.

The interaction between anchoring and framing was not merely additive but often multiplicative. High anchor + loss frame combinations in the procurement and diplomatic cases produced the most prolonged negotiations and the largest counter offers, reinforcing Tan observations of non-linear bias dynamics [11]. Low anchor + gain frame pairings in the job-offer and trade agreement cases led to the fastest agreements but tended to favor the party setting the anchor. These patterns underline the importance of considering both biases simultaneously in negotiation analysis, rather than treating them as isolated variables.

All three tested interventions-choice mindset, AI decision-support, and bias-awareness training—reduced susceptibility to anchoring and framing, but their relative performance varied by context.: Choice mindset was especially effective in individual-level, resource-constrained settings (e.g., job offers), where prompting negotiators to generate multiple counteroffers increased deviation from anchors. AI decision-support tools were most impactful in data-rich contexts (e.g., procurement), providing objective benchmarks and counter anchor suggestions that significantly reduced the anchor’s pull. Bias-awareness training excelled in politically sensitive negotiations (e.g., diplomacy), where relational considerations were as important as numerical outcomes, helping negotiators reframe offers and counter anchors in ways that preserved goodwill.

The most substantial reductions in bias effects occurred when interventions were combined—such as pairing a choice mindset with AI support. In these scenarios, deviations from initial anchors were largest, counteroffer creativity was highest, and final settlements were most favorable for the intervention group. This suggests an additive or synergistic effect when cognitive and technological debiasing measures are deployed together.

5. Discussion

The expanded synthesis of results across simulated and field-informed cases highlights the enduring relevance of anchoring and framing biases in negotiation and the importance of tackling them as interacting phenomena rather than independent effects. By triangulating findings from multiple contexts, job-offer negotiations, procurement contracts, and diplomatic trade talks, this study reinforces theoretical propositions, offers nuanced practical implications, and identifies fertile ground for future research.

The results confirm that anchoring effects persist even among experienced negotiators and in information-rich contexts. This suggests that anchoring is a cognitively deep-rooted heuristic, resistant to mere exposure to objective benchmarks unless deliberate countermeasures are employed.

Farming consistently altered negotiator behavior in line with Prospect Theory and recent experimental findings. Loss frames increased counteroffer magnitude, reflecting risk-seeking tendencies, while gain frames promoted faster agreements but also increased susceptibility to anchor influence.

The multiplicative nature of anchor–frame interactions-particularly the high anchor + loss frame combination producing both prolonged negotiations and larger concessions-supports the modeling work on non-linear bias dynamics [12]. This finding underscores the importance of multi-bias frameworks in negotiation research and training.

Choice mindset was most impactful in individual, resource-constrained negotiations, such as job offers, where generating multiple counteroffers directly weakened anchor fixation.

AI decision-support tools proved decisive in procurement contexts by offering credible, external benchmarks and counter anchors, disrupting both the psychological pull of the anchor and the persuasive influence of the frame.

Bias-awareness training was particularly effective in diplomacy, where economic outcomes must be balanced with relational capital; training helped negotiators neutralize loss frames and counter anchors without eroding goodwill.

The synthesis showed that pairing cognitive (choice mindset) and technological (e.g., AI) tools produced the largest deviations from anchors, the highest counter-offer creativity, and the most favorable settlements. This suggests that negotiation preparation should prioritize multi-pronged debiasing strategies, especially for high-value or high-stakes contexts.

This study advances negotiation scholarship by providing integrated empirical evidence of anchoring–framing interactions across multiple contexts and domains; demonstrating that bias susceptibility is context-dependent and that different interventions excel under different conditions; and offering evidence for additive benefits when cognitive and technological debiasing tools are combined-an underexplored area in negotiation research.

While the findings are robust, several limitations remain. Simulated and workshop-based field settings cannot fully replicate the political and emotional stakes of life, high-risk negotiations. The AI tool’s effectiveness depends heavily on data quality and relevance; rapidly shifting markets may reduce their utility. Cultural variables-such as tolerance for confrontation or preference for indirect communication-were not systematically explored, leaving room for future cross-cultural bias interaction studies.

6. Conclusion

This paper has examined anchoring and framing as two of the most powerful and persistent cognitive biases influencing negotiation outcomes. By synthesizing empirical studies from 2020–2025, proposing a mixed-methods research framework, and illustrating bias dynamics through three contextually rich case examples, this study has demonstrated that these biases not only operate independently but can also interact in multiplicative ways that significantly shape both the process and results of bargaining.

The findings reinforce that anchors set the psychological reference point for negotiation, exerting influence even in information-rich environments and among experienced professionals. Frames shift risk preferences and concession strategies, with loss frames generally prompting greater resistance and more aggressive counter offers and gain frames promoting faster agreement but often favoring the anchor setter. Combined biases, especially high anchors presented in loss frames, produce disproportionate impacts, highlighting the need for integrated bias-management strategies.

This study has also shown that bias mitigation is achievable when interventions are context-matched and multi-layered. Choice mindset priming disrupts anchor fixation in individual-level negotiations. AI-based decision-support tools provide credible benchmarks and counter anchor suggestions in data-rich settings. Bias-awareness training enhances framing detection and counter anchoring in relationship-sensitive contexts such as diplomacy. Importantly, when these interventions are combined, they produce additive or even synergistic effects, delivering the most favorable negotiation outcomes.

For practitioners, the implication is clear: treat every offer as containing both a numeric anchor and a rhetorical frame, diagnose them separately, and deploy tailored interventions based on the negotiation’s context, stakes, and complexity. For scholars, the challenge lies in building integrated, multi-bias models that incorporate cultural differences, multi-party dynamics, and evolving AI capabilities for real-time bias detection and adaptive support.

Ultimately, mastering cognitive bias management in negotiation is not just about avoiding costly mistakes, it is about actively shaping the negotiation environment to enable agreements that are more equitable, efficient, and sustainable. Those who understand and strategically navigate these biases will not only protect their interests but also create greater value at the table.